--------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\Jonas Kaiser\Desktop\Replication\Replication\Replication_Main_Log.log
  log type:  text
 opened on:   6 Feb 2025, 08:49:14

. 
. *Creates folders
. cap mkdir "Figures"

. cap mkdir "Tables"

. 
. *Clear all existing data
. clear all

. 
. *Load data
. use "Data_Clean.dta"

. 
. *Define controls 
. glo controls1 "Age Male Ethnicitydum1 Ethnicitydum2 Ethnicitydum3 Ethnicitydum4 Schoolingdum1 Schoolingdum3 
> Schoolingdum4 Schoolingdum5 Schoolingdum6 Schoolingdum7 Employed"

. glo controls2 "Age Male Ethnicitydum1 Ethnicitydum2 Ethnicitydum3 Ethnicitydum4 Schoolingdum1 Schoolingdum3 
> Schoolingdum4 Schoolingdum5 Schoolingdum6 Schoolingdum7 Employed Political_Interest StrongSupporter Democrat
> "

. glo controls1_logit "Age i.Male i.Ethnicitydum1 i.Ethnicitydum2 i.Ethnicitydum3 i.Ethnicitydum4 i.Schoolingd
> um1 i.Schoolingdum3 i.Schoolingdum4 i.Schoolingdum5 i.Schoolingdum6 i.Schoolingdum7 i.Employed"

. glo controls2_logit "Age i.Male i.Ethnicitydum1 i.Ethnicitydum2 i.Ethnicitydum3 i.Ethnicitydum4 i.Schoolingd
> um1 i.Schoolingdum3 i.Schoolingdum4 i.Schoolingdum5 i.Schoolingdum6 i.Schoolingdum7 i.Employed Political_Int
> erest i.StrongSupporter i.Democrat"

. 
. ********************************************************************************
. *** FIGURE 2
. ********************************************************************************
. 
. *Generate and save first plot
. vioplot FT_InParty, over(Democrat) xlabel(1 "Republicans" 2 "Democrats", nogrid) ytitle(Feeling Thermometer)
>  xscale(noline) density(color(gs8)) bar(color(black)) line(color(black)) title("Own Party") ylabel(0(20)100)

. graph save figures/temp1.gph, replace
(file figures/temp1.gph not found)
file figures/temp1.gph saved

. *Generate and save second plot 
. vioplot FT_OutParty, over(Democrat) xlabel(1 "Republicans" 2 "Democrats", nogrid) ytitle("") xscale(noline) 
> density(color(gs8)) bar(color(black)) line(color(black)) title("Opposite Party") yscale(off) 

. graph save figures/temp2.gph, replace
(file figures/temp2.gph not found)
file figures/temp2.gph saved

. *Combine plots
. graph combine figures/temp1.gph figures/temp2.gph, ycommon xcommon imargin(0 0) note() 

. *Export figure
. graph export figures/fig2.eps, replace
(file figures/fig2.eps not found)
file figures/fig2.eps saved as EPS format

. 
. ********************************************************************************
. *** TABLE 2
. ********************************************************************************
. 
. *Estimate and store column 1 regression
. reg AP_FT Invasion_Condition if Disagreement_Condition==0, robust

Linear regression                               Number of obs     =        926
                                                F(1, 924)         =       1.81
                                                Prob > F          =     0.1786
                                                R-squared         =     0.0020
                                                Root MSE          =     28.296

------------------------------------------------------------------------------------
                   |               Robust
             AP_FT | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
Invasion_Condition |  -2.504218   1.860181    -1.35   0.179    -6.154888    1.146452
             _cons |   51.43404    1.30041    39.55   0.000     48.88194    53.98614
------------------------------------------------------------------------------------

. eststo AP_FT1_H1

. *Estimate and store column 2 regression
. reg AP_FT Invasion_Condition $controls1 if Disagreement_Condition==0, robust

Linear regression                               Number of obs     =        926
                                                F(14, 911)        =       2.41
                                                Prob > F          =     0.0025
                                                R-squared         =     0.0334
                                                Root MSE          =     28.046

------------------------------------------------------------------------------------
                   |               Robust
             AP_FT | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
Invasion_Condition |  -2.475977   1.854673    -1.33   0.182    -6.115904    1.163951
               Age |   .2056896   .0665344     3.09   0.002      .075111    .3362682
              Male |   .4351288   1.856233     0.23   0.815     -3.20786    4.078118
     Ethnicitydum1 |  -8.603084   3.815894    -2.25   0.024    -16.09205   -1.114119
     Ethnicitydum2 |  -3.133423   4.099392    -0.76   0.445    -11.17877    4.911927
     Ethnicitydum3 |  -3.053606   4.664099    -0.65   0.513    -12.20723    6.100021
     Ethnicitydum4 |  -1.456108   10.00672    -0.15   0.884    -21.09502     18.1828
     Schoolingdum1 |  -3.423958   3.292745    -1.04   0.299    -9.886206     3.03829
     Schoolingdum3 |  -1.053569   5.067635    -0.21   0.835    -10.99917    8.892028
     Schoolingdum4 |  -7.882851   3.133702    -2.52   0.012    -14.03296   -1.732737
     Schoolingdum5 |   -2.01832   9.851374    -0.20   0.838    -21.35234    17.31571
     Schoolingdum6 |  -5.124736   3.044108    -1.68   0.093    -11.09902    .8495435
     Schoolingdum7 |  -4.466515   2.494108    -1.79   0.074     -9.36138    .4283498
          Employed |    .078006   2.109022     0.04   0.971      -4.0611    4.217112
             _cons |     46.806   3.828273    12.23   0.000     39.29274    54.31926
------------------------------------------------------------------------------------

. eststo AP_FT2_H1

. *Estimate and store column 3 regression
. reg AP_FT Invasion_Condition $controls2 if Disagreement_Condition==0, robust

Linear regression                               Number of obs     =        926
                                                F(17, 908)        =      28.02
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3204
                                                Root MSE          =     23.554

------------------------------------------------------------------------------------
                   |               Robust
             AP_FT | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
Invasion_Condition |  -2.858869   1.562939    -1.83   0.068    -5.926263    .2085246
               Age |   .1054912   .0574671     1.84   0.067    -.0072927     .218275
              Male |   .1340274   1.577122     0.08   0.932    -2.961201    3.229256
     Ethnicitydum1 |  -2.225779   3.083658    -0.72   0.471    -8.277706    3.826147
     Ethnicitydum2 |  -4.658144   3.623075    -1.29   0.199    -11.76872    2.452431
     Ethnicitydum3 |  -4.385677   4.215194    -1.04   0.298    -12.65833    3.886979
     Ethnicitydum4 |   1.240766   8.373589     0.15   0.882    -15.19307     17.6746
     Schoolingdum1 |  -.5577132   2.836151    -0.20   0.844    -6.123887    5.008461
     Schoolingdum3 |  -1.526386    4.16185    -0.37   0.714     -9.69435    6.641578
     Schoolingdum4 |  -5.494612   2.726343    -2.02   0.044    -10.84528   -.1439456
     Schoolingdum5 |  -.8337726   9.797397    -0.09   0.932    -20.06195     18.3944
     Schoolingdum6 |  -6.012995   2.583194    -2.33   0.020    -11.08272    -.943271
     Schoolingdum7 |  -5.085077   2.043916    -2.49   0.013    -9.096426   -1.073728
          Employed |  -1.392168   1.803111    -0.77   0.440    -4.930918    2.146582
Political_Interest |   4.779728   .9251994     5.17   0.000      2.96395    6.595506
   StrongSupporter |    25.3348   1.776576    14.26   0.000     21.84812    28.82147
          Democrat |    2.68722   1.640832     1.64   0.102    -.5330431    5.907483
             _cons |   21.59765   3.824945     5.65   0.000     14.09089    29.10442
------------------------------------------------------------------------------------

. eststo AP_FT3_H1

. *Estimate and store column 4 regression
. reg AP_FT Invasion_Condition if Control_Condition==0, robust

Linear regression                               Number of obs     =        933
                                                F(1, 931)         =       0.06
                                                Prob > F          =     0.8053
                                                R-squared         =     0.0001
                                                Root MSE          =     28.234

------------------------------------------------------------------------------------
                   |               Robust
             AP_FT | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
Invasion_Condition |     .45603   1.849632     0.25   0.805    -3.173902    4.085962
             _cons |   48.47379   1.285286    37.71   0.000      45.9514    50.99619
------------------------------------------------------------------------------------

. eststo AP_FT1_H3

. *Estimate and store column 5 regression
. reg AP_FT Invasion_Condition $controls1 if Control_Condition==0, robust

Linear regression                               Number of obs     =        933
                                                F(14, 918)        =       2.94
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0419
                                                Root MSE          =     27.832

------------------------------------------------------------------------------------
                   |               Robust
             AP_FT | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
Invasion_Condition |   .8392706   1.851885     0.45   0.651    -2.795149     4.47369
               Age |   .3170707    .064967     4.88   0.000     .1895697    .4445718
              Male |   .4232923    1.83734     0.23   0.818    -3.182581    4.029166
     Ethnicitydum1 |  -7.933259   3.907061    -2.03   0.043    -15.60107   -.2654502
     Ethnicitydum2 |   1.196575   4.547562     0.26   0.793    -7.728249     10.1214
     Ethnicitydum3 |  -9.717717   5.114904    -1.90   0.058    -19.75598    .3205445
     Ethnicitydum4 |  -3.250941   7.431063    -0.44   0.662    -17.83478     11.3329
     Schoolingdum1 |  -.6018637   3.123766    -0.19   0.847    -6.732416    5.528689
     Schoolingdum3 |  -1.314333   4.263086    -0.31   0.758    -9.680858    7.052192
     Schoolingdum4 |  -1.043494   2.828878    -0.37   0.712    -6.595313    4.508326
     Schoolingdum5 |  -8.451881   9.975165    -0.85   0.397    -28.02865    11.12489
     Schoolingdum6 |  -2.097346   3.046511    -0.69   0.491    -8.076282    3.881589
     Schoolingdum7 |  -1.007691   2.628271    -0.38   0.702    -6.165809    4.150426
          Employed |   2.054296   2.097532     0.98   0.328    -2.062219    6.170811
             _cons |   35.78745   3.748408     9.55   0.000     28.43101     43.1439
------------------------------------------------------------------------------------

. eststo AP_FT2_H3

. *Estimate and store column 6 regression
. reg AP_FT Invasion_Condition $controls2 if Control_Condition==0, robust

Linear regression                               Number of obs     =        933
                                                F(17, 915)        =      24.91
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2982
                                                Root MSE          =     23.859

------------------------------------------------------------------------------------
                   |               Robust
             AP_FT | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
Invasion_Condition |  -.4442484    1.58144    -0.28   0.779     -3.54792    2.659423
               Age |   .2251745   .0570918     3.94   0.000     .1131284    .3372207
              Male |   .1545457   1.639739     0.09   0.925     -3.06354    3.372632
     Ethnicitydum1 |  -6.169739   3.069834    -2.01   0.045    -12.19447   -.1450049
     Ethnicitydum2 |  -4.100689   3.646281    -1.12   0.261    -11.25673    3.055355
     Ethnicitydum3 |   -10.4118   4.436673    -2.35   0.019    -19.11904   -1.704565
     Ethnicitydum4 |  -2.227692   7.521652    -0.30   0.767    -16.98939      12.534
     Schoolingdum1 |   .9312354   2.732253     0.34   0.733    -4.430975    6.293446
     Schoolingdum3 |  -1.626851   3.514738    -0.46   0.644    -8.524735    5.271032
     Schoolingdum4 |   .0591992   2.566521     0.02   0.982    -4.977753    5.096151
     Schoolingdum5 |  -8.399736   7.854541    -1.07   0.285    -23.81474    7.015273
     Schoolingdum6 |  -3.177411   2.663999    -1.19   0.233    -8.405669    2.050847
     Schoolingdum7 |  -.9801969   2.233075    -0.44   0.661     -5.36274    3.402346
          Employed |   1.873743   1.808487     1.04   0.300    -1.675522    5.423007
Political_Interest |   3.088688   .9068029     3.41   0.001     1.309033    4.868343
   StrongSupporter |   24.01055   1.757488    13.66   0.000     20.56138    27.45973
          Democrat |   7.161535   1.644641     4.35   0.000     3.933829    10.38924
             _cons |   14.96016   3.726559     4.01   0.000     7.646569    22.27376
------------------------------------------------------------------------------------

. eststo AP_FT3_H3

. 
. *Combine regression outputs and export
. esttab AP_FT1_H1 AP_FT2_H1 AP_FT3_H1 AP_FT1_H3 AP_FT2_H3 AP_FT3_H3 using Tables/Table2.tex, replace b(%12.3f
> ) se(%12.3f) nomtitles starlevels(* 0.10 ** 0.05 *** 0.01) stats(N r2_a, fmt(0 2) labels(N "Adj. R2")) title
> ("Regressions of difference in own/opposite-party Feeling Thermometer rating on Treatment and controls.") or
> der(Invasion_Condition Disagreement_Condition Age Male Ethnicitydum1 Ethnicitydum2 Ethnicitydum3 Ethnicitydu
> m4 Schoolingdum5 Schoolingdum4 Schoolingdum7 Schoolingdum1 Schoolingdum4 Schoolingdum3) varlabels(_cons Cons
> tant Invasion_Condition "Invasion" Disagreement_Condition "Disagreement" Ethnicitydum1 "Asian American" Ethn
> icitydum2 "African American" Ethnicitydum3 "Hispanic" Ethnicitydum4 "Other Ethnicity" Schoolingdum1 "Associa
> te Degree" Schoolingdum3 "Doctorate Degree" Schoolingdum4 "HS Degree" Schoolingdum5 "Less than HS Degree" Sc
> hoolingdum6 "Master's Degree" Schoolingdum7 "Some College" Political_Interest "Political Interest" StrongSup
> porter "Strong Supporter")  
(file Tables/Table2.tex not found)
(output written to Tables/Table2.tex)

. 
. *Clear stored estimates 
. eststo clear

. 
. ********************************************************************************
. *** FIGURE 3
. ********************************************************************************
. 
. *Generate temp. variables for plot
. gen run=_n

. gen pp=. 
(1,403 missing values generated)

. replace pp=1 if run<350
(349 real changes made)

. replace pp=3 if run>=350 & run<700
(350 real changes made)

. replace pp=5 if run>=700 & run<1050
(350 real changes made)

. replace pp=7 if run>=1050
(354 real changes made)

. 
. *Manually generate numbers for the bars
. gen share1=.
(1,403 missing values generated)

. gen share1_hi=.
(1,403 missing values generated)

. gen share1_li=.
(1,403 missing values generated)

. sum P1_Type2 if AP_FT<26

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    P1_Type2 |        350    .0571429    .2324477          0          1

. replace share1=.0571429*100 if pp==1 
(349 real changes made)

. replace share1_hi=share1+invttail(350-1,0.025)*(0.2324477/sqrt(350))*100 if pp==1
(349 real changes made)

. replace share1_li=share1-invttail(350-1,0.025)*(0.2324477/sqrt(350))*100 if pp==1
(349 real changes made)

. gen share2=.
(1,403 missing values generated)

. gen share2_hi=.
(1,403 missing values generated)

. gen share2_li=.
(1,403 missing values generated)

. sum P1_Type2 if AP_FT>=26 & AP_FT<50

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    P1_Type2 |        261    .0574713     .233188          0          1

. replace share2=.0574713*100 if pp==3 
(350 real changes made)

. replace share2_hi=share2+invttail(261-1,0.025)*(0.233188/sqrt(261))*100 if pp==3
(350 real changes made)

. replace share2_li=share2-invttail(261-1,0.025)*(0.233188/sqrt(261))*100 if pp==3
(350 real changes made)

. gen share3=.
(1,403 missing values generated)

. gen share3_hi=.
(1,403 missing values generated)

. gen share3_li=.
(1,403 missing values generated)

. sum P1_Type2 if AP_FT>=50 & AP_FT<70

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    P1_Type2 |        384    .1276042    .3340838          0          1

. replace share3=.1276042*100 if pp==5 
(350 real changes made)

. replace share3_hi=share3+invttail(384-1,0.025)*(0.3340838/sqrt(384))*100 if pp==5
(350 real changes made)

. replace share3_li=share3-invttail(384-1,0.025)*(0.3340838/sqrt(384))*100 if pp==5
(350 real changes made)

. gen share4=.
(1,403 missing values generated)

. gen share4_hi=.
(1,403 missing values generated)

. gen share4_li=.
(1,403 missing values generated)

. sum P1_Type2 if AP_FT>=70

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    P1_Type2 |        408    .1911765    .3937105          0          1

. replace share4=.1911765*100 if pp==7
(354 real changes made)

. replace share4_hi=share4+invttail(417-1,0.025)*(0.3937105/sqrt(417))*100 if pp==7
(354 real changes made)

. replace share4_li=share4-invttail(417-1,0.025)*(0.3937105/sqrt(417))*100 if pp==7       
(354 real changes made)

. 
. *Produce and save the graph
. graph twoway (bar share1 pp, color(red*1.2)) (rcap share1_hi share1_li pp, color(black)) (bar share2 pp, col
> or(red*0.8)) (rcap share2_hi share2_li pp, color(black)) (bar share3 pp, color(blue*0.8)) (rcap share3_hi sh
> are3_li pp, color(black)) (bar share4 pp, color(blue*1.2)) (rcap share4_hi share4_li pp, color(black)), note
> ("") title("") xlabel(1 "1st Quartile FT" 3 "2nd Quartile FT" 5 "3rd Quartile FT" 7 "4th Quartile FT", nogri
> d) xscale(noline) xtitle("") ytitle("Affective Polarization in aBoS: Type BA (%)") ylabel(0(5)30) yscale(ran
> ge(0 30)) legend(off)

. graph export figures/fig3.eps, replace 
(file figures/fig3.eps not found)
file figures/fig3.eps saved as EPS format

. 
. *Drop temp. variables 
. drop run pp share1 share1_hi share1_li share2 share2_hi share2_li share3 share3_hi share3_li share4 share4_h
> i share4_li

. 
. ********************************************************************************
. *** FIGURE 4
. ********************************************************************************
. 
. *Generate temp. variables for plot
. gen run=_n

. gen tt=. 
(1,403 missing values generated)

. replace tt=1 if run<200
(199 real changes made)

. replace tt=2 if run>=200 & run<400
(200 real changes made)

. replace tt=3 if run>=400 & run<600
(200 real changes made)

. replace tt=5 if run>=600 & run<800
(200 real changes made)

. replace tt=6 if run>=800 & run<1000
(200 real changes made)

. replace tt=7 if run>=1000
(404 real changes made)

. 
. *Manually generate numbers for the bars
. gen dec_inv=.
(1,403 missing values generated)

. gen hi_inv=.
(1,403 missing values generated)

. gen li_inv=. 
(1,403 missing values generated)

. replace dec_inv=0.495614*100 if tt==1
(199 real changes made)

. replace hi_inv=dec_inv+invttail(456-1,0.025)*(0.5005299/sqrt(456))*100 if tt==1
(199 real changes made)

. replace li_inv=dec_inv-invttail(456-1,0.025)*(0.5005299/sqrt(456))*100 if tt==1
(199 real changes made)

. replace dec_inv=0.4210526*100 if tt==5
(200 real changes made)

. replace hi_inv=dec_inv+invttail(456-1,0.025)*(0.4942702/sqrt(456))*100 if tt==5
(200 real changes made)

. replace li_inv=dec_inv-invttail(456-1,0.025)*(0.4942702/sqrt(456))*100 if tt==5
(200 real changes made)

. gen dec_con=. 
(1,403 missing values generated)

. gen hi_con=.
(1,403 missing values generated)

. gen li_con=. 
(1,403 missing values generated)

. replace dec_con=0.4425532*100 if tt==2
(200 real changes made)

. replace hi_con=dec_con+invttail(470-1,0.025)*(0.4972181/sqrt(470))*100 if tt==2
(200 real changes made)

. replace li_con=dec_con-invttail(470-1,0.025)*(0.4972181/sqrt(470))*100 if tt==2
(200 real changes made)

. replace dec_con=0.3617021*100 if tt==6
(200 real changes made)

. replace hi_con=dec_con+invttail(470-1,0.025)*(0.4810052/sqrt(470))*100 if tt==6
(200 real changes made)

. replace li_con=dec_con-invttail(470-1,0.025)*(0.4810052/sqrt(470))*100 if tt==6
(200 real changes made)

. gen dec_dis=.
(1,403 missing values generated)

. gen hi_dis=.
(1,403 missing values generated)

. gen li_dis=.
(1,403 missing values generated)

. replace dec_dis=0.475891*100 if tt==3
(200 real changes made)

. replace hi_dis=dec_dis+invttail(477-1,0.025)*(0.4999427/sqrt(477))*100 if tt==3
(200 real changes made)

. replace li_dis=dec_dis-invttail(477-1,0.025)*(0.4999427/sqrt(477))*100 if tt==3
(200 real changes made)

. replace dec_dis=0.3962264*100 if tt==7
(404 real changes made)

. replace hi_dis=dec_dis+invttail(477-1,0.025)*(0.489626/sqrt(477))*100 if tt==7
(404 real changes made)

. replace li_dis=dec_dis-invttail(477-1,0.025)*(0.489626/sqrt(477))*100 if tt==7
(404 real changes made)

. 
. *Produce and save the graph
. graph twoway (bar dec_inv tt, color(red*1.2)) (rcap hi_inv li_inv tt, color(black)) (bar dec_con tt, color(g
> s8)) (rcap hi_con li_con tt, color(black)) (bar dec_dis tt, color(sand)) (rcap hi_dis li_dis tt, color(black
> )), title("") xlabel(2 "Own-Party Condition" 6 "Opposite-Party Condition", nogrid labsize(medlarge)) xscale(
> noline) xtitle("") ytitle("Choosing B (%)", size(large)) ylabel(0(10)70) yscale(range(0 70)) ylabel(0(10)70,
>  labsize(medlarge)) legend(order(1 "Invasion" 3 "Control" 5 "Disagreement") size(medlarge)) 

. graph export figures/fig4.eps, replace 
(file figures/fig4.eps not found)
file figures/fig4.eps saved as EPS format

. 
. *Drop temp. variables 
. drop run tt dec_inv hi_inv li_inv dec_con hi_con li_con dec_dis hi_dis li_dis 

. 
. ********************************************************************************
. *** ALL ADDITIONAL RESULTS IN THE PAPER 
. ********************************************************************************
. 
. *Hedges g (affective polarization in feeling thermometers)
. esize twosample AP_FT if Disagreement_Condition==0, by(Invasion_Condition) all

Effect size based on mean comparison

                               Obs per group:
                       Invasion_Condition==0 =        470
                       Invasion_Condition==1 =        456
---------------------------------------------------------
        Effect size |   Estimate     [95% conf. interval]
--------------------+------------------------------------
          Cohen's d |   .0884993    -.0404193    .2173701
         Hedges's g |   .0884275    -.0403865    .2171936
    Glass's Delta 1 |    .088828    -.0401761    .2177376
    Glass's Delta 2 |   .0881643    -.0408428    .2170748
   Point-biserial r |   .0442491    -.0202251    .1081521
---------------------------------------------------------

. esize twosample AP_FT if Invasion_Condition==0, by(Disagreement_Condition) all

Effect size based on mean comparison

                               Obs per group:
                   Disagreement_Condition==0 =        470
                   Disagreement_Condition==1 =        477
---------------------------------------------------------
        Effect size |   Estimate     [95% conf. interval]
--------------------+------------------------------------
          Cohen's d |    .105232    -.0222681    .2326765
         Hedges's g |   .1051484    -.0222505    .2324918
    Glass's Delta 1 |    .105004    -.0226128    .2325093
    Glass's Delta 2 |    .105458    -.0221572    .2329627
   Point-biserial r |   .0525973    -.0111449    .1156763
---------------------------------------------------------

. esize twosample AP_FT if Control_Condition==0, by(Invasion_Condition) all

Effect size based on mean comparison

                               Obs per group:
                       Invasion_Condition==0 =        477
                       Invasion_Condition==1 =        456
---------------------------------------------------------
        Effect size |   Estimate     [95% conf. interval]
--------------------+------------------------------------
          Cohen's d |  -.0161518    -.1445148    .1122198
         Hedges's g |  -.0161388    -.1443984    .1121294
    Glass's Delta 1 |  -.0162459    -.1446068    .1121319
    Glass's Delta 2 |  -.0160551    -.1444158    .1123231
   Point-biserial r |  -.0080823    -.0721283    .0560676
---------------------------------------------------------

. 
. *aBoS descriptive statistics: 
. tab P1_InParty, freq

 P1_InParty |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        742       52.89       52.89
          1 |        661       47.11      100.00
------------+-----------------------------------
      Total |      1,403      100.00

. tab P1_OutParty, freq

P1_OutParty |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        852       60.73       60.73
          1 |        551       39.27      100.00
------------+-----------------------------------
      Total |      1,403      100.00

. tab P1_InParty P1_OutParty, col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

           |      P1_OutParty
P1_InParty |         0          1 |     Total
-----------+----------------------+----------
         0 |       690         52 |       742 
           |     80.99       9.44 |     52.89 
-----------+----------------------+----------
         1 |       162        499 |       661 
           |     19.01      90.56 |     47.11 
-----------+----------------------+----------
     Total |       852        551 |     1,403 
           |    100.00     100.00 |    100.00 

. mcci 690 52 162 499     //McNemar 

                 |        Controls        |
Cases            |   Exposed   Unexposed  |      Total
-----------------+------------------------+-----------
         Exposed |       690          52  |        742
       Unexposed |       162         499  |        661
-----------------+------------------------+-----------
           Total |       852         551  |       1403

McNemar's chi2(1) =     56.54    Prob > chi2 = 0.0000
Exact McNemar significance probability       = 0.0000

Proportion with factor
        Cases       .5288667
        Controls    .6072701     [95% conf. interval]
                   ---------     --------------------
        difference -.0784034     -.0991362  -.0576706
        ratio        .870892      .8400466     .90287
        rel. diff.  -.199637     -.2566309  -.1426431

        odds ratio  .3209877       .230192   .4411932   (exact)

. 
. tab P1_Type

  All types |
     in one |
   variable |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        499       35.57       35.57
          2 |        162       11.55       47.11
          3 |        690       49.18       96.29
          4 |         52        3.71      100.00
------------+-----------------------------------
      Total |      1,403      100.00

. 
. *Multinomial logit to estimate relation between behavior as Player 1 in aBoS and FT difference 
. mlogit P1_Type AP_FT, base(1) robust

Iteration 0:  Log pseudolikelihood = -1526.5915  
Iteration 1:  Log pseudolikelihood = -1497.2368  
Iteration 2:  Log pseudolikelihood = -1496.2831  
Iteration 3:  Log pseudolikelihood = -1496.2798  
Iteration 4:  Log pseudolikelihood = -1496.2798  

Multinomial logistic regression                         Number of obs =  1,403
                                                        Wald chi2(3)  =  57.70
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -1496.2798                       Pseudo R2     = 0.0199

------------------------------------------------------------------------------
             |               Robust
     P1_Type | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
       AP_FT |   .0260981   .0035422     7.37   0.000     .0191555    .0330408
       _cons |  -2.531274   .2304534   -10.98   0.000    -2.982954   -2.079593
-------------+----------------------------------------------------------------
3            |
       AP_FT |   .0085841   .0020869     4.11   0.000     .0044939    .0126744
       _cons |  -.0824273   .1150735    -0.72   0.474    -.3079673    .1431127
-------------+----------------------------------------------------------------
4            |
       AP_FT |   .0044365   .0053132     0.83   0.404    -.0059772    .0148503
       _cons |  -2.464226   .2901453    -8.49   0.000      -3.0329   -1.895551
------------------------------------------------------------------------------

. margins, dydx(AP_FT) atmeans post

Conditional marginal effects                             Number of obs = 1,403
Model VCE: Robust

dy/dx wrt: AP_FT

1._predict: Pr(P1_Type==1), predict(pr outcome(1))
2._predict: Pr(P1_Type==2), predict(pr outcome(2))
3._predict: Pr(P1_Type==3), predict(pr outcome(3))
4._predict: Pr(P1_Type==4), predict(pr outcome(4))

At: AP_FT = 49.61368 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
AP_FT        |
    _predict |
          1  |  -.0025589   .0004512    -5.67   0.000    -.0034433   -.0016745
          2  |   .0019577   .0002817     6.95   0.000     .0014056    .0025098
          3  |   .0007049   .0004774     1.48   0.140    -.0002308    .0016407
          4  |  -.0001037   .0001881    -0.55   0.581    -.0004723     .000265
------------------------------------------------------------------------------

. mlogit P1_Type AP_FT $controls1_logit, base(1) robust

Iteration 0:  Log pseudolikelihood = -1526.5915  
Iteration 1:  Log pseudolikelihood = -1456.2612  
Iteration 2:  Log pseudolikelihood = -1454.2352  
Iteration 3:  Log pseudolikelihood = -1454.1173  
Iteration 4:  Log pseudolikelihood = -1454.0963  
Iteration 5:  Log pseudolikelihood = -1454.0917  
Iteration 6:  Log pseudolikelihood = -1454.0905  
Iteration 7:  Log pseudolikelihood = -1454.0903  
Iteration 8:  Log pseudolikelihood = -1454.0902  
Iteration 9:  Log pseudolikelihood = -1454.0902  

Multinomial logistic regression                        Number of obs =   1,403
                                                       Wald chi2(42) = 1641.09
                                                       Prob > chi2   =  0.0000
Log pseudolikelihood = -1454.0902                      Pseudo R2     =  0.0475

---------------------------------------------------------------------------------
                |               Robust
        P1_Type | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
1               |  (base outcome)
----------------+----------------------------------------------------------------
2               |
          AP_FT |   .0265072   .0037067     7.15   0.000     .0192422    .0337723
            Age |   .0056484    .006742     0.84   0.402    -.0075656    .0188625
         1.Male |  -.3451179   .1891236    -1.82   0.068    -.7157933    .0255575
1.Ethnicitydum1 |  -.0347753   .4221919    -0.08   0.934    -.8622561    .7927056
1.Ethnicitydum2 |   .8505181   .4557748     1.87   0.062    -.0427841     1.74382
1.Ethnicitydum3 |  -.4558777   .5613299    -0.81   0.417    -1.556064    .6443086
1.Ethnicitydum4 |   .6129476   .6702215     0.91   0.360    -.7006625    1.926558
1.Schoolingdum1 |   .0628835   .4027306     0.16   0.876    -.7264539    .8522209
1.Schoolingdum3 |  -.1206915   .4570164    -0.26   0.792    -1.016427    .7750441
1.Schoolingdum4 |     .70518   .3061175     2.30   0.021     .1052008    1.305159
1.Schoolingdum5 |  -12.60673   .6596046   -19.11   0.000    -13.89953   -11.31393
1.Schoolingdum6 |  -.0165419   .2812546    -0.06   0.953    -.5677907     .534707
1.Schoolingdum7 |   .2305139   .2483177     0.93   0.353    -.2561798    .7172076
     1.Employed |   .3185468    .211978     1.50   0.133    -.0969225    .7340161
          _cons |  -2.983333   .4262322    -7.00   0.000    -3.818733   -2.147933
----------------+----------------------------------------------------------------
3               |
          AP_FT |   .0088974   .0021779     4.09   0.000     .0046288    .0131661
            Age |   .0084266   .0043759     1.93   0.054    -.0001499    .0170032
         1.Male |  -.5216081   .1228922    -4.24   0.000    -.7624724   -.2807437
1.Ethnicitydum1 |   .1865836   .2472736     0.75   0.451    -.2980639     .671231
1.Ethnicitydum2 |   .8374961    .335467     2.50   0.013     .1799928    1.494999
1.Ethnicitydum3 |   .4090762   .2742705     1.49   0.136    -.1284841    .9466364
1.Ethnicitydum4 |  -.0577325   .5416385    -0.11   0.915    -1.119325     1.00386
1.Schoolingdum1 |   .5833636   .2303268     2.53   0.011     .1319314    1.034796
1.Schoolingdum3 |  -.4703767   .3168503    -1.48   0.138    -1.091392    .1506384
1.Schoolingdum4 |   .4496604   .2102367     2.14   0.032      .037604    .8617168
1.Schoolingdum5 |   1.121046   .7356616     1.52   0.128    -.3208242    2.562916
1.Schoolingdum6 |  -.4740331   .1882931    -2.52   0.012    -.8430807   -.1049854
1.Schoolingdum7 |  -.2362145   .1675026    -1.41   0.158    -.5645135    .0920846
     1.Employed |   .2235768   .1411027     1.58   0.113    -.0529795    .5001331
          _cons |  -.3788178   .2551647    -1.48   0.138    -.8789315    .1212959
----------------+----------------------------------------------------------------
4               |
          AP_FT |    .004485   .0054562     0.82   0.411    -.0062089     .015179
            Age |   .0034268   .0120423     0.28   0.776    -.0201756    .0270293
         1.Male |   .1596627   .3045969     0.52   0.600    -.4373363    .7566617
1.Ethnicitydum1 |  -.4922329   .7452395    -0.66   0.509    -1.952875    .9684096
1.Ethnicitydum2 |  -.3053119   1.069378    -0.29   0.775    -2.401254     1.79063
1.Ethnicitydum3 |   .4239357   .5958233     0.71   0.477    -.7438566    1.591728
1.Ethnicitydum4 |   .3134348   1.083694     0.29   0.772    -1.810567    2.437437
1.Schoolingdum1 |   1.018687   .4797007     2.12   0.034     .0784913    1.958883
1.Schoolingdum3 |  -.1062921   .8009511    -0.13   0.894    -1.676127    1.463543
1.Schoolingdum4 |    .750968   .4559301     1.65   0.100    -.1426387    1.644575
1.Schoolingdum5 |   1.739878   1.240843     1.40   0.161    -.6921291    4.171885
1.Schoolingdum6 |  -.3560351    .530334    -0.67   0.502    -1.395471    .6834004
1.Schoolingdum7 |   .1521984   .3858251     0.39   0.693    -.6040049    .9084016
     1.Employed |   .0174946   .3101727     0.06   0.955    -.5904328     .625422
          _cons |  -2.901753   .5690441    -5.10   0.000    -4.017059   -1.786447
---------------------------------------------------------------------------------

. margins, dydx(AP_FT) atmeans post

Conditional marginal effects                             Number of obs = 1,403
Model VCE: Robust

dy/dx wrt: AP_FT

1._predict: Pr(P1_Type==1), predict(pr outcome(1))
2._predict: Pr(P1_Type==2), predict(pr outcome(2))
3._predict: Pr(P1_Type==3), predict(pr outcome(3))
4._predict: Pr(P1_Type==4), predict(pr outcome(4))

At: AP_FT           = 49.61368 (mean)
    Age             = 40.00285 (mean)
    0.Male          = .5010691 (mean)
    1.Male          = .4989309 (mean)
    0.Ethnicitydum1 = .9337135 (mean)
    1.Ethnicitydum1 = .0662865 (mean)
    0.Ethnicitydum2 =  .955809 (mean)
    1.Ethnicitydum2 =  .044191 (mean)
    0.Ethnicitydum3 = .9472559 (mean)
    1.Ethnicitydum3 = .0527441 (mean)
    0.Ethnicitydum4 = .9857448 (mean)
    1.Ethnicitydum4 = .0142552 (mean)
    0.Schoolingdum1 = .9016393 (mean)
    1.Schoolingdum1 = .0983607 (mean)
    0.Schoolingdum3 = .9600855 (mean)
    1.Schoolingdum3 = .0399145 (mean)
    0.Schoolingdum4 = .8788311 (mean)
    1.Schoolingdum4 = .1211689 (mean)
    0.Schoolingdum5 = .9921597 (mean)
    1.Schoolingdum5 = .0078403 (mean)
    0.Schoolingdum6 = .8638632 (mean)
    1.Schoolingdum6 = .1361368 (mean)
    0.Schoolingdum7 = .8011404 (mean)
    1.Schoolingdum7 = .1988596 (mean)
    0.Employed      = .2758375 (mean)
    1.Employed      = .7241625 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
AP_FT        |
    _predict |
          1  |  -.0025773   .0004712    -5.47   0.000    -.0035008   -.0016537
          2  |   .0018167   .0002729     6.66   0.000      .001282    .0023515
          3  |   .0008597   .0004964     1.73   0.083    -.0001132    .0018326
          4  |  -.0000992   .0001849    -0.54   0.592    -.0004617    .0002633
------------------------------------------------------------------------------

. mlogit P1_Type AP_FT $controls2_logit, base(1) robust

Iteration 0:  Log pseudolikelihood = -1526.5915  
Iteration 1:  Log pseudolikelihood = -1437.5158  
Iteration 2:  Log pseudolikelihood = -1435.2194  
Iteration 3:  Log pseudolikelihood = -1435.1037  
Iteration 4:  Log pseudolikelihood = -1435.0814  
Iteration 5:  Log pseudolikelihood = -1435.0766  
Iteration 6:  Log pseudolikelihood = -1435.0755  
Iteration 7:  Log pseudolikelihood = -1435.0752  
Iteration 8:  Log pseudolikelihood = -1435.0752  
Iteration 9:  Log pseudolikelihood = -1435.0752  

Multinomial logistic regression                        Number of obs =   1,403
                                                       Wald chi2(51) = 1957.69
                                                       Prob > chi2   =  0.0000
Log pseudolikelihood = -1435.0752                      Pseudo R2     =  0.0599

------------------------------------------------------------------------------------
                   |               Robust
           P1_Type | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------------+----------------------------------------------------------------
1                  |  (base outcome)
-------------------+----------------------------------------------------------------
2                  |
             AP_FT |    .025827   .0042922     6.02   0.000     .0174144    .0342397
               Age |    .005211   .0067729     0.77   0.442    -.0080637    .0184856
            1.Male |   -.333022   .1950047    -1.71   0.088    -.7152242    .0491802
   1.Ethnicitydum1 |  -.0131539   .4248954    -0.03   0.975    -.8459335    .8196258
   1.Ethnicitydum2 |   .9071041   .4631009     1.96   0.050    -.0005571    1.814765
   1.Ethnicitydum3 |  -.4474614   .5595601    -0.80   0.424    -1.544179    .6492562
   1.Ethnicitydum4 |    .715288   .6793008     1.05   0.292    -.6161171    2.046693
   1.Schoolingdum1 |   .0638822   .4079055     0.16   0.876     -.735598    .8633623
   1.Schoolingdum3 |  -.0996913   .4581513    -0.22   0.828    -.9976514    .7982688
   1.Schoolingdum4 |   .7256717   .3090955     2.35   0.019     .1198556    1.331488
   1.Schoolingdum5 |   -13.0391   .6460147   -20.18   0.000    -14.30527   -11.77294
   1.Schoolingdum6 |  -.0153841   .2823594    -0.05   0.957    -.5687984    .5380303
   1.Schoolingdum7 |   .2229943   .2483316     0.90   0.369    -.2637268    .7097154
        1.Employed |   .3198116   .2135298     1.50   0.134    -.0986991    .7383223
Political_Interest |  -.0272021    .111965    -0.24   0.808    -.2466496    .1922453
 1.StrongSupporter |    .198075   .2278186     0.87   0.385    -.2484411    .6445912
        1.Democrat |  -.1874355   .1955043    -0.96   0.338    -.5706169     .195746
             _cons |  -2.850182   .5045335    -5.65   0.000     -3.83905   -1.861315
-------------------+----------------------------------------------------------------
3                  |
             AP_FT |   .0138207   .0026386     5.24   0.000     .0086491    .0189923
               Age |   .0074733   .0044982     1.66   0.097    -.0013431    .0162896
            1.Male |   -.513764   .1255595    -4.09   0.000    -.7598561   -.2676719
   1.Ethnicitydum1 |   .2834392   .2465121     1.15   0.250    -.1997156     .766594
   1.Ethnicitydum2 |   1.091912   .3355745     3.25   0.001     .4341976    1.749625
   1.Ethnicitydum3 |   .4509648   .2817928     1.60   0.110     -.101339    1.003269
   1.Ethnicitydum4 |   .0634719   .5560829     0.11   0.909    -1.026431    1.153374
   1.Schoolingdum1 |   .5314653   .2372433     2.24   0.025      .066477    .9964535
   1.Schoolingdum3 |   -.386788   .3233582    -1.20   0.232    -1.020558    .2469824
   1.Schoolingdum4 |   .4310092   .2155143     2.00   0.046      .008609    .8534094
   1.Schoolingdum5 |   1.161738   .7223997     1.61   0.108    -.2541392    2.577616
   1.Schoolingdum6 |  -.4244444   .1900738    -2.23   0.026    -.7969822   -.0519066
   1.Schoolingdum7 |  -.2624072    .169117    -1.55   0.121    -.5938705    .0690561
        1.Employed |   .2129763   .1429163     1.49   0.136    -.0671345    .4930871
Political_Interest |  -.0847621   .0727775    -1.16   0.244    -.2274035    .0578792
 1.StrongSupporter |  -.1976871   .1497722    -1.32   0.187    -.4912352    .0958611
        1.Democrat |  -.6336362   .1273567    -4.98   0.000    -.8832507   -.3840216
             _cons |   .1085495   .3075387     0.35   0.724    -.4942152    .7113142
-------------------+----------------------------------------------------------------
4                  |
             AP_FT |   .0092465   .0065014     1.42   0.155    -.0034959    .0219889
               Age |   .0012507   .0120315     0.10   0.917    -.0223307    .0248321
            1.Male |   .1458777   .3165532     0.46   0.645    -.4745552    .7663106
   1.Ethnicitydum1 |  -.3449019   .7461261    -0.46   0.644    -1.807282    1.117478
   1.Ethnicitydum2 |   .0027916   1.069991     0.00   0.998    -2.094353    2.099936
   1.Ethnicitydum3 |   .4722876   .5956919     0.79   0.428     -.695247    1.639822
   1.Ethnicitydum4 |   .4929063   1.081293     0.46   0.648    -1.626389    2.612202
   1.Schoolingdum1 |   .9605137   .4801329     2.00   0.045     .0194705    1.901557
   1.Schoolingdum3 |  -.0378472   .8006881    -0.05   0.962    -1.607167    1.531473
   1.Schoolingdum4 |   .7425685   .4638414     1.60   0.109    -.1665439    1.651681
   1.Schoolingdum5 |   1.783554   1.262874     1.41   0.158    -.6916337    4.258742
   1.Schoolingdum6 |  -.3216216   .5263827    -0.61   0.541    -1.353313    .7100695
   1.Schoolingdum7 |   .1222456   .3882212     0.31   0.753     -.638654    .8831453
        1.Employed |  -.0125224   .3108228    -0.04   0.968    -.6217238     .596679
Political_Interest |   .0034078    .146689     0.02   0.981    -.2840974     .290913
 1.StrongSupporter |  -.2396779    .349542    -0.69   0.493    -.9247676    .4454119
        1.Democrat |  -.7931516   .3082384    -2.57   0.010    -1.397288   -.1890155
             _cons |  -2.544377   .6870067    -3.70   0.000    -3.890886   -1.197869
------------------------------------------------------------------------------------

. margins, dydx(AP_FT) atmeans post

Conditional marginal effects                             Number of obs = 1,403
Model VCE: Robust

dy/dx wrt: AP_FT

1._predict: Pr(P1_Type==1), predict(pr outcome(1))
2._predict: Pr(P1_Type==2), predict(pr outcome(2))
3._predict: Pr(P1_Type==3), predict(pr outcome(3))
4._predict: Pr(P1_Type==4), predict(pr outcome(4))

At: AP_FT              = 49.61368 (mean)
    Age                = 40.00285 (mean)
    0.Male             = .5010691 (mean)
    1.Male             = .4989309 (mean)
    0.Ethnicitydum1    = .9337135 (mean)
    1.Ethnicitydum1    = .0662865 (mean)
    0.Ethnicitydum2    =  .955809 (mean)
    1.Ethnicitydum2    =  .044191 (mean)
    0.Ethnicitydum3    = .9472559 (mean)
    1.Ethnicitydum3    = .0527441 (mean)
    0.Ethnicitydum4    = .9857448 (mean)
    1.Ethnicitydum4    = .0142552 (mean)
    0.Schoolingdum1    = .9016393 (mean)
    1.Schoolingdum1    = .0983607 (mean)
    0.Schoolingdum3    = .9600855 (mean)
    1.Schoolingdum3    = .0399145 (mean)
    0.Schoolingdum4    = .8788311 (mean)
    1.Schoolingdum4    = .1211689 (mean)
    0.Schoolingdum5    = .9921597 (mean)
    1.Schoolingdum5    = .0078403 (mean)
    0.Schoolingdum6    = .8638632 (mean)
    1.Schoolingdum6    = .1361368 (mean)
    0.Schoolingdum7    = .8011404 (mean)
    1.Schoolingdum7    = .1988596 (mean)
    0.Employed         = .2758375 (mean)
    1.Employed         = .7241625 (mean)
    Political_Interest = 3.344262 (mean)
    0.StrongSupporter  = .5196009 (mean)
    1.StrongSupporter  = .4803991 (mean)
    0.Democrat         = .4918033 (mean)
    1.Democrat         = .5081967 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
AP_FT        |
    _predict |
          1  |  -.0035086   .0005691    -6.17   0.000     -.004624   -.0023932
          2  |   .0014994   .0003285     4.56   0.000     .0008555    .0021433
          3  |   .0020317   .0006022     3.37   0.001     .0008513    .0032121
          4  |  -.0000225   .0002189    -0.10   0.918    -.0004516    .0004066
------------------------------------------------------------------------------

. 
. *Logit regressions (treatment effects on Player 1 decision in aBoS
. logit P1_InParty i.Invasion_Condition if Disagreement_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -640.03668  
Iteration 1:  Log pseudolikelihood = -638.72777  
Iteration 2:  Log pseudolikelihood = -638.72776  

Logistic regression                                     Number of obs =    926
                                                        Wald chi2(1)  =   2.61
                                                        Prob > chi2   = 0.1061
Log pseudolikelihood = -638.72776                       Pseudo R2     = 0.0020

--------------------------------------------------------------------------------------
                     |               Robust
          P1_InParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .2132621   .1319693     1.62   0.106     -.045393    .4719172
               _cons |  -.2308064   .0929183    -2.48   0.013    -.4129229   -.0486899
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 926
Model VCE: Robust

Expression: Pr(P1_InParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition = .5075594 (mean)
    1.Invasion_Condition = .4924406 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0530608   .0327759     1.62   0.105    -.0111787    .1173003
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_InParty i.Invasion_Condition $controls1_logit if Disagreement_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -640.03668  
Iteration 1:  Log pseudolikelihood = -618.18222  
Iteration 2:  Log pseudolikelihood = -618.11663  
Iteration 3:  Log pseudolikelihood = -618.11653  
Iteration 4:  Log pseudolikelihood = -618.11653  

Logistic regression                                     Number of obs =    926
                                                        Wald chi2(14) =  41.55
                                                        Prob > chi2   = 0.0001
Log pseudolikelihood = -618.11653                       Pseudo R2     = 0.0342

--------------------------------------------------------------------------------------
                     |               Robust
          P1_InParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |    .248731   .1354476     1.84   0.066    -.0167413    .5142033
                 Age |  -.0075498   .0048845    -1.55   0.122    -.0171233    .0020236
              1.Male |   .4852612   .1375539     3.53   0.000     .2156605     .754862
     1.Ethnicitydum1 |  -.0127023   .2645559    -0.05   0.962    -.5312223    .5058177
     1.Ethnicitydum2 |  -.5487099    .330443    -1.66   0.097    -1.196366    .0989465
     1.Ethnicitydum3 |  -.3639321   .2989647    -1.22   0.223    -.9498921     .222028
     1.Ethnicitydum4 |   .5659471   .6500212     0.87   0.384     -.708071    1.839965
     1.Schoolingdum1 |  -.3024584   .2497079    -1.21   0.226     -.791877    .1869602
     1.Schoolingdum3 |   .4314825   .3590377     1.20   0.229    -.2722184    1.135183
     1.Schoolingdum4 |  -.1247379   .2332639    -0.53   0.593    -.5819268    .3324509
     1.Schoolingdum5 |  -1.193411   .7633761    -1.56   0.118      -2.6896     .302779
     1.Schoolingdum6 |   .4975934    .213655     2.33   0.020     .0788372    .9163496
     1.Schoolingdum7 |   .4636686   .1836917     2.52   0.012     .1036395    .8236976
          1.Employed |   .0575486     .15807     0.36   0.716    -.2522629    .3673601
               _cons |  -.3198688   .2854777    -1.12   0.263    -.8793947    .2396572
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 926
Model VCE: Robust

Expression: Pr(P1_InParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition = .5075594 (mean)
    1.Invasion_Condition = .4924406 (mean)
    Age                  = 40.22678 (mean)
    0.Male               = .5075594 (mean)
    1.Male               = .4924406 (mean)
    0.Ethnicitydum1      = .9265659 (mean)
    1.Ethnicitydum1      = .0734341 (mean)
    0.Ethnicitydum2      = .9524838 (mean)
    1.Ethnicitydum2      = .0475162 (mean)
    0.Ethnicitydum3      = .9427646 (mean)
    1.Ethnicitydum3      = .0572354 (mean)
    0.Ethnicitydum4      =  .987041 (mean)
    1.Ethnicitydum4      =  .012959 (mean)
    0.Schoolingdum1      = .9060475 (mean)
    1.Schoolingdum1      = .0939525 (mean)
    0.Schoolingdum3      =  .962203 (mean)
    1.Schoolingdum3      =  .037797 (mean)
    0.Schoolingdum4      = .8844492 (mean)
    1.Schoolingdum4      = .1155508 (mean)
    0.Schoolingdum5      = .9892009 (mean)
    1.Schoolingdum5      = .0107991 (mean)
    0.Schoolingdum6      = .8639309 (mean)
    1.Schoolingdum6      = .1360691 (mean)
    0.Schoolingdum7      = .7840173 (mean)
    1.Schoolingdum7      = .2159827 (mean)
    0.Employed           = .2807775 (mean)
    1.Employed           = .7192225 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0618384   .0335924     1.84   0.066    -.0040015    .1276783
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_InParty i.Invasion_Condition $controls2_logit if Disagreement_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -640.03668  
Iteration 1:  Log pseudolikelihood = -605.74135  
Iteration 2:  Log pseudolikelihood = -605.60347  
Iteration 3:  Log pseudolikelihood = -605.60345  

Logistic regression                                     Number of obs =    926
                                                        Wald chi2(17) =  61.26
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -605.60345                       Pseudo R2     = 0.0538

--------------------------------------------------------------------------------------
                     |               Robust
          P1_InParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .2592685   .1375199     1.89   0.059    -.0102655    .5288025
                 Age |  -.0072707   .0049956    -1.46   0.146    -.0170619    .0025205
              1.Male |   .4898118   .1405101     3.49   0.000     .2144171    .7652065
     1.Ethnicitydum1 |   -.106024   .2634862    -0.40   0.687    -.6224475    .4103995
     1.Ethnicitydum2 |   -.811769   .3341482    -2.43   0.015    -1.466687   -.1568507
     1.Ethnicitydum3 |  -.3961245   .3060853    -1.29   0.196    -.9960407    .2037917
     1.Ethnicitydum4 |   .4060542   .6702964     0.61   0.545    -.9077026    1.719811
     1.Schoolingdum1 |  -.2390441    .255808    -0.93   0.350    -.7404186    .2623303
     1.Schoolingdum3 |   .3155884   .3648743     0.86   0.387    -.3995521    1.030729
     1.Schoolingdum4 |  -.0742532   .2331249    -0.32   0.750    -.5311697    .3826633
     1.Schoolingdum5 |  -1.246141   .7986198    -1.56   0.119    -2.811407    .3191248
     1.Schoolingdum6 |   .4589644    .219199     2.09   0.036     .0293423    .8885865
     1.Schoolingdum7 |   .5000058   .1857686     2.69   0.007      .135906    .8641055
          1.Employed |   .0745605   .1583516     0.47   0.638    -.2358029    .3849239
  Political_Interest |   .0830667   .0786131     1.06   0.291    -.0710123    .2371456
   1.StrongSupporter |   .1204919   .1500349     0.80   0.422    -.1735711     .414555
          1.Democrat |   .6215121   .1429454     4.35   0.000     .3413442    .9016801
               _cons |  -.9975008   .3655804    -2.73   0.006    -1.714025   -.2809763
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 926
Model VCE: Robust

Expression: Pr(P1_InParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition = .5075594 (mean)
    1.Invasion_Condition = .4924406 (mean)
    Age                  = 40.22678 (mean)
    0.Male               = .5075594 (mean)
    1.Male               = .4924406 (mean)
    0.Ethnicitydum1      = .9265659 (mean)
    1.Ethnicitydum1      = .0734341 (mean)
    0.Ethnicitydum2      = .9524838 (mean)
    1.Ethnicitydum2      = .0475162 (mean)
    0.Ethnicitydum3      = .9427646 (mean)
    1.Ethnicitydum3      = .0572354 (mean)
    0.Ethnicitydum4      =  .987041 (mean)
    1.Ethnicitydum4      =  .012959 (mean)
    0.Schoolingdum1      = .9060475 (mean)
    1.Schoolingdum1      = .0939525 (mean)
    0.Schoolingdum3      =  .962203 (mean)
    1.Schoolingdum3      =  .037797 (mean)
    0.Schoolingdum4      = .8844492 (mean)
    1.Schoolingdum4      = .1155508 (mean)
    0.Schoolingdum5      = .9892009 (mean)
    1.Schoolingdum5      = .0107991 (mean)
    0.Schoolingdum6      = .8639309 (mean)
    1.Schoolingdum6      = .1360691 (mean)
    0.Schoolingdum7      = .7840173 (mean)
    1.Schoolingdum7      = .2159827 (mean)
    0.Employed           = .2807775 (mean)
    1.Employed           = .7192225 (mean)
    Political_Interest   =  3.37257 (mean)
    0.StrongSupporter    = .5064795 (mean)
    1.StrongSupporter    = .4935205 (mean)
    0.Democrat           = .4902808 (mean)
    1.Democrat           = .5097192 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0644337   .0340908     1.89   0.059    -.0023829    .1312504
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_OutParty i.Invasion_Condition if Disagreement_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -619.64374  
Iteration 1:  Log pseudolikelihood = -617.93125  
Iteration 2:  Log pseudolikelihood = -617.93094  
Iteration 3:  Log pseudolikelihood = -617.93094  

Logistic regression                                     Number of obs =    926
                                                        Wald chi2(1)  =   3.42
                                                        Prob > chi2   = 0.0646
Log pseudolikelihood = -617.93094                       Pseudo R2     = 0.0028

--------------------------------------------------------------------------------------
                     |               Robust
         P1_OutParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .2495303   .1350244     1.85   0.065    -.0151126    .5141733
               _cons |   -.567984   .0960502    -5.91   0.000    -.7562391    -.379729
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 926
Model VCE: Robust

Expression: Pr(P1_OutParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition = .5075594 (mean)
    1.Invasion_Condition = .4924406 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0593505   .0320454     1.85   0.064    -.0034573    .1221583
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_OutParty i.Invasion_Condition $controls1_logit if Disagreement_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -619.64374  
Iteration 1:  Log pseudolikelihood =  -597.6233  
Iteration 2:  Log pseudolikelihood = -597.51542  
Iteration 3:  Log pseudolikelihood = -597.51538  

Logistic regression                                     Number of obs =    926
                                                        Wald chi2(14) =  39.63
                                                        Prob > chi2   = 0.0003
Log pseudolikelihood = -597.51538                       Pseudo R2     = 0.0357

--------------------------------------------------------------------------------------
                     |               Robust
         P1_OutParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .2731975   .1378786     1.98   0.048     .0029604    .5434345
                 Age |  -.0130474    .005093    -2.56   0.010    -.0230296   -.0030653
              1.Male |   .5556514   .1406465     3.95   0.000     .2799893    .8313135
     1.Ethnicitydum1 |  -.1166631   .2619381    -0.45   0.656    -.6300524    .3967263
     1.Ethnicitydum2 |  -.8212737   .3771343    -2.18   0.029    -1.560443   -.0821041
     1.Ethnicitydum3 |  -.1887013   .2986005    -0.63   0.527    -.7739476     .396545
     1.Ethnicitydum4 |   -.258068   .6708838    -0.38   0.700    -1.572976     1.05684
     1.Schoolingdum1 |  -.0297831   .2530904    -0.12   0.906    -.5258311    .4662649
     1.Schoolingdum3 |   .3137712   .3681534     0.85   0.394    -.4077962    1.035338
     1.Schoolingdum4 |  -.2113981   .2487232    -0.85   0.395    -.6988867    .2760904
     1.Schoolingdum5 |  -.2145728   .6621287    -0.32   0.746    -1.512321    1.083176
     1.Schoolingdum6 |   .4883465   .2183834     2.24   0.025     .0603229    .9163702
     1.Schoolingdum7 |   .4305501   .1852749     2.32   0.020     .0674179    .7936823
          1.Employed |  -.0070615   .1630343    -0.04   0.965    -.3266029    .3124799
               _cons |  -.4253717     .28781    -1.48   0.139    -.9894689    .1387254
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 926
Model VCE: Robust

Expression: Pr(P1_OutParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition = .5075594 (mean)
    1.Invasion_Condition = .4924406 (mean)
    Age                  = 40.22678 (mean)
    0.Male               = .5075594 (mean)
    1.Male               = .4924406 (mean)
    0.Ethnicitydum1      = .9265659 (mean)
    1.Ethnicitydum1      = .0734341 (mean)
    0.Ethnicitydum2      = .9524838 (mean)
    1.Ethnicitydum2      = .0475162 (mean)
    0.Ethnicitydum3      = .9427646 (mean)
    1.Ethnicitydum3      = .0572354 (mean)
    0.Ethnicitydum4      =  .987041 (mean)
    1.Ethnicitydum4      =  .012959 (mean)
    0.Schoolingdum1      = .9060475 (mean)
    1.Schoolingdum1      = .0939525 (mean)
    0.Schoolingdum3      =  .962203 (mean)
    1.Schoolingdum3      =  .037797 (mean)
    0.Schoolingdum4      = .8844492 (mean)
    1.Schoolingdum4      = .1155508 (mean)
    0.Schoolingdum5      = .9892009 (mean)
    1.Schoolingdum5      = .0107991 (mean)
    0.Schoolingdum6      = .8639309 (mean)
    1.Schoolingdum6      = .1360691 (mean)
    0.Schoolingdum7      = .7840173 (mean)
    1.Schoolingdum7      = .2159827 (mean)
    0.Employed           = .2807775 (mean)
    1.Employed           = .7192225 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0646508   .0325288     1.99   0.047     .0008956    .1284059
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_OutParty i.Invasion_Condition $controls2_logit if Disagreement_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -619.64374  
Iteration 1:  Log pseudolikelihood = -591.05616  
Iteration 2:  Log pseudolikelihood = -590.87087  
Iteration 3:  Log pseudolikelihood = -590.87079  
Iteration 4:  Log pseudolikelihood = -590.87079  

Logistic regression                                     Number of obs =    926
                                                        Wald chi2(17) =  52.05
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -590.87079                       Pseudo R2     = 0.0464

--------------------------------------------------------------------------------------
                     |               Robust
         P1_OutParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .2836766   .1388998     2.04   0.041     .0114379    .5559153
                 Age |  -.0112672   .0051668    -2.18   0.029    -.0213939   -.0011406
              1.Male |   .5661808   .1431767     3.95   0.000     .2855595     .846802
     1.Ethnicitydum1 |  -.2915502   .2707354    -1.08   0.282    -.8221819    .2390815
     1.Ethnicitydum2 |  -1.027248   .3854361    -2.67   0.008    -1.782689    -.271807
     1.Ethnicitydum3 |  -.1941797    .310502    -0.63   0.532    -.8027524     .414393
     1.Ethnicitydum4 |   -.450775   .6420733    -0.70   0.483    -1.709215    .8076655
     1.Schoolingdum1 |  -.0090745   .2579541    -0.04   0.972    -.5146553    .4965063
     1.Schoolingdum3 |   .2264628   .3704257     0.61   0.541    -.4995583    .9524839
     1.Schoolingdum4 |  -.2045109    .248558    -0.82   0.411    -.6916757    .2826538
     1.Schoolingdum5 |   -.222961   .6714126    -0.33   0.740    -1.538905    1.092983
     1.Schoolingdum6 |   .4700006   .2213831     2.12   0.034     .0360976    .9039035
     1.Schoolingdum7 |   .4678496   .1863239     2.51   0.012     .1026614    .8330378
          1.Employed |   .0276453   .1641711     0.17   0.866    -.2941241    .3494147
  Political_Interest |  -.0102577   .0795154    -0.13   0.897    -.1661049    .1455896
   1.StrongSupporter |  -.2547968   .1531136    -1.66   0.096    -.5548939    .0453002
          1.Democrat |   .4995936   .1449004     3.45   0.001      .215594    .7835933
               _cons |  -.6131863   .3640955    -1.68   0.092      -1.3268    .1004277
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 926
Model VCE: Robust

Expression: Pr(P1_OutParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition = .5075594 (mean)
    1.Invasion_Condition = .4924406 (mean)
    Age                  = 40.22678 (mean)
    0.Male               = .5075594 (mean)
    1.Male               = .4924406 (mean)
    0.Ethnicitydum1      = .9265659 (mean)
    1.Ethnicitydum1      = .0734341 (mean)
    0.Ethnicitydum2      = .9524838 (mean)
    1.Ethnicitydum2      = .0475162 (mean)
    0.Ethnicitydum3      = .9427646 (mean)
    1.Ethnicitydum3      = .0572354 (mean)
    0.Ethnicitydum4      =  .987041 (mean)
    1.Ethnicitydum4      =  .012959 (mean)
    0.Schoolingdum1      = .9060475 (mean)
    1.Schoolingdum1      = .0939525 (mean)
    0.Schoolingdum3      =  .962203 (mean)
    1.Schoolingdum3      =  .037797 (mean)
    0.Schoolingdum4      = .8844492 (mean)
    1.Schoolingdum4      = .1155508 (mean)
    0.Schoolingdum5      = .9892009 (mean)
    1.Schoolingdum5      = .0107991 (mean)
    0.Schoolingdum6      = .8639309 (mean)
    1.Schoolingdum6      = .1360691 (mean)
    0.Schoolingdum7      = .7840173 (mean)
    1.Schoolingdum7      = .2159827 (mean)
    0.Employed           = .2807775 (mean)
    1.Employed           = .7192225 (mean)
    Political_Interest   =  3.37257 (mean)
    0.StrongSupporter    = .5064795 (mean)
    1.StrongSupporter    = .4935205 (mean)
    0.Democrat           = .4902808 (mean)
    1.Democrat           = .5097192 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0670156   .0327224     2.05   0.041     .0028809    .1311503
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_InParty i.Invasion_Condition if Control_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -646.31559  
Iteration 1:  Log pseudolikelihood = -646.13405  
Iteration 2:  Log pseudolikelihood = -646.13405  

Logistic regression                                     Number of obs =    933
                                                        Wald chi2(1)  =   0.36
                                                        Prob > chi2   = 0.5471
Log pseudolikelihood = -646.13405                       Pseudo R2     = 0.0003

--------------------------------------------------------------------------------------
                     |               Robust
          P1_InParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0789666   .1311347     0.60   0.547    -.1780528     .335986
               _cons |  -.0965109   .0917295    -1.05   0.293    -.2762975    .0832757
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 933
Model VCE: Robust

Expression: Pr(P1_InParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition = .511254 (mean)
    1.Invasion_Condition = .488746 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |    .019723   .0327451     0.60   0.547    -.0444562    .0839023
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_InParty i.Invasion_Condition $controls1_logit if Control_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -646.31559  
Iteration 1:  Log pseudolikelihood = -624.93204  
Iteration 2:  Log pseudolikelihood = -624.88486  
Iteration 3:  Log pseudolikelihood = -624.88486  

Logistic regression                                     Number of obs =    933
                                                        Wald chi2(14) =  40.55
                                                        Prob > chi2   = 0.0002
Log pseudolikelihood = -624.88486                       Pseudo R2     = 0.0332

--------------------------------------------------------------------------------------
                     |               Robust
          P1_InParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0859722   .1350701     0.64   0.524    -.1787603    .3507047
                 Age |  -.0036314   .0048081    -0.76   0.450    -.0130551    .0057924
              1.Male |   .2790773   .1361449     2.05   0.040     .0122382    .5459165
     1.Ethnicitydum1 |   .1025604   .2824389     0.36   0.717    -.4510097    .6561306
     1.Ethnicitydum2 |  -.3868013    .320387    -1.21   0.227    -1.014748    .2411457
     1.Ethnicitydum3 |   -.555775   .3216947    -1.73   0.084    -1.186285    .0747351
     1.Ethnicitydum4 |   .1615913   .5305163     0.30   0.761    -.8782015    1.201384
     1.Schoolingdum1 |  -.7071148   .2442813    -2.89   0.004    -1.185897   -.2283324
     1.Schoolingdum3 |   .1340587   .3383262     0.40   0.692    -.5290484    .7971658
     1.Schoolingdum4 |   -.509013   .2275935    -2.24   0.025     -.955088    -.062938
     1.Schoolingdum5 |  -1.140656   .7863499    -1.45   0.147    -2.681874    .4005612
     1.Schoolingdum6 |   .4635409   .2118811     2.19   0.029     .0482616    .8788202
     1.Schoolingdum7 |   .3434547   .1922492     1.79   0.074    -.0333469    .7202562
          1.Employed |  -.1123937   .1565683    -0.72   0.473    -.4192619    .1944745
               _cons |   .0292438   .2730132     0.11   0.915    -.5058523    .5643399
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 933
Model VCE: Robust

Expression: Pr(P1_InParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition =  .511254 (mean)
    1.Invasion_Condition =  .488746 (mean)
    Age                  =  39.6538 (mean)
    0.Male               = .4951768 (mean)
    1.Male               = .5048232 (mean)
    0.Ethnicitydum1      = .9378349 (mean)
    1.Ethnicitydum1      = .0621651 (mean)
    0.Ethnicitydum2      = .9528403 (mean)
    1.Ethnicitydum2      = .0471597 (mean)
    0.Ethnicitydum3      = .9485531 (mean)
    1.Ethnicitydum3      = .0514469 (mean)
    0.Ethnicitydum4      = .9849946 (mean)
    1.Ethnicitydum4      = .0150054 (mean)
    0.Schoolingdum1      = .8971061 (mean)
    1.Schoolingdum1      = .1028939 (mean)
    0.Schoolingdum3      = .9571275 (mean)
    1.Schoolingdum3      = .0428725 (mean)
    0.Schoolingdum4      = .8788853 (mean)
    1.Schoolingdum4      = .1211147 (mean)
    0.Schoolingdum5      = .9914255 (mean)
    1.Schoolingdum5      = .0085745 (mean)
    0.Schoolingdum6      =   .86388 (mean)
    1.Schoolingdum6      =   .13612 (mean)
    0.Schoolingdum7      = .8199357 (mean)
    1.Schoolingdum7      = .1800643 (mean)
    0.Employed           =  .278671 (mean)
    1.Employed           =  .721329 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0214681   .0337192     0.64   0.524    -.0446202    .0875564
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_InParty i.Invasion_Condition $controls2_logit if Control_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -646.31559  
Iteration 1:  Log pseudolikelihood = -619.27246  
Iteration 2:  Log pseudolikelihood = -619.18775  
Iteration 3:  Log pseudolikelihood = -619.18773  

Logistic regression                                     Number of obs =    933
                                                        Wald chi2(17) =  49.05
                                                        Prob > chi2   = 0.0001
Log pseudolikelihood = -619.18773                       Pseudo R2     = 0.0420

--------------------------------------------------------------------------------------
                     |               Robust
          P1_InParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0825673   .1360587     0.61   0.544    -.1841027    .3492374
                 Age |  -.0033171    .004954    -0.67   0.503    -.0130267    .0063925
              1.Male |   .2698036   .1410527     1.91   0.056    -.0066547    .5462619
     1.Ethnicitydum1 |   .0224359   .2850235     0.08   0.937       -.5362    .5810717
     1.Ethnicitydum2 |   -.536469   .3214399    -1.67   0.095     -1.16648    .0935417
     1.Ethnicitydum3 |  -.5568086   .3271221    -1.70   0.089    -1.197956     .084339
     1.Ethnicitydum4 |   .0736358   .5456442     0.13   0.893    -.9958072    1.143079
     1.Schoolingdum1 |  -.6580647   .2477051    -2.66   0.008    -1.143558   -.1725717
     1.Schoolingdum3 |   .1089805   .3411494     0.32   0.749      -.55966     .777621
     1.Schoolingdum4 |  -.4727347   .2292602    -2.06   0.039    -.9220765   -.0233929
     1.Schoolingdum5 |  -1.166939   .7999469    -1.46   0.145    -2.734806    .4009284
     1.Schoolingdum6 |   .4435842   .2139687     2.07   0.038     .0242133    .8629551
     1.Schoolingdum7 |   .3647789   .1937836     1.88   0.060    -.0150299    .7445878
          1.Employed |  -.1172112   .1571964    -0.75   0.456    -.4253104    .1908881
  Political_Interest |   .0314997   .0773429     0.41   0.684    -.1200896     .183089
   1.StrongSupporter |   .0853426   .1471712     0.58   0.562    -.2031077    .3737929
          1.Democrat |   .4272693   .1407548     3.04   0.002      .151395    .7031436
               _cons |  -.3334197   .3343861    -1.00   0.319    -.9888043    .3219649
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 933
Model VCE: Robust

Expression: Pr(P1_InParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition =  .511254 (mean)
    1.Invasion_Condition =  .488746 (mean)
    Age                  =  39.6538 (mean)
    0.Male               = .4951768 (mean)
    1.Male               = .5048232 (mean)
    0.Ethnicitydum1      = .9378349 (mean)
    1.Ethnicitydum1      = .0621651 (mean)
    0.Ethnicitydum2      = .9528403 (mean)
    1.Ethnicitydum2      = .0471597 (mean)
    0.Ethnicitydum3      = .9485531 (mean)
    1.Ethnicitydum3      = .0514469 (mean)
    0.Ethnicitydum4      = .9849946 (mean)
    1.Ethnicitydum4      = .0150054 (mean)
    0.Schoolingdum1      = .8971061 (mean)
    1.Schoolingdum1      = .1028939 (mean)
    0.Schoolingdum3      = .9571275 (mean)
    1.Schoolingdum3      = .0428725 (mean)
    0.Schoolingdum4      = .8788853 (mean)
    1.Schoolingdum4      = .1211147 (mean)
    0.Schoolingdum5      = .9914255 (mean)
    1.Schoolingdum5      = .0085745 (mean)
    0.Schoolingdum6      =   .86388 (mean)
    1.Schoolingdum6      =   .13612 (mean)
    0.Schoolingdum7      = .8199357 (mean)
    1.Schoolingdum7      = .1800643 (mean)
    0.Employed           =  .278671 (mean)
    1.Employed           =  .721329 (mean)
    Political_Interest   = 3.315113 (mean)
    0.StrongSupporter    = .5209003 (mean)
    1.StrongSupporter    = .4790997 (mean)
    0.Democrat           = .4908896 (mean)
    1.Democrat           = .5091104 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0206172   .0339655     0.61   0.544    -.0459539    .0871883
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_OutParty i.Invasion_Condition if Control_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -630.94697  
Iteration 1:  Log pseudolikelihood = -630.64962  
Iteration 2:  Log pseudolikelihood = -630.64961  

Logistic regression                                     Number of obs =    933
                                                        Wald chi2(1)  =   0.59
                                                        Prob > chi2   = 0.4409
Log pseudolikelihood = -630.64961                       Pseudo R2     = 0.0005

--------------------------------------------------------------------------------------
                     |               Robust
         P1_OutParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .1027597    .133336     0.77   0.441    -.1585741    .3640935
               _cons |  -.4212135   .0936623    -4.50   0.000    -.6047882   -.2376387
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 933
Model VCE: Robust

Expression: Pr(P1_OutParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition = .511254 (mean)
    1.Invasion_Condition = .488746 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0248262   .0322059     0.77   0.441    -.0382962    .0879487
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_OutParty i.Invasion_Condition $controls1_logit if Control_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -630.94697  
Iteration 1:  Log pseudolikelihood = -606.37128  
Iteration 2:  Log pseudolikelihood = -606.20726  
Iteration 3:  Log pseudolikelihood = -606.20725  

Logistic regression                                     Number of obs =    933
                                                        Wald chi2(14) =  45.85
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -606.20725                       Pseudo R2     = 0.0392

--------------------------------------------------------------------------------------
                     |               Robust
         P1_OutParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .1069248   .1378796     0.78   0.438    -.1633142    .3771638
                 Age |  -.0091511   .0048954    -1.87   0.062    -.0187459    .0004436
              1.Male |   .5204437     .13886     3.75   0.000     .2482831    .7926043
     1.Ethnicitydum1 |   .1606236   .2792865     0.58   0.565    -.3867679    .7080152
     1.Ethnicitydum2 |  -.6842396   .3633052    -1.88   0.060    -1.396305    .0278256
     1.Ethnicitydum3 |  -.2545168    .322815    -0.79   0.430    -.8872226     .378189
     1.Ethnicitydum4 |   .2370971   .5678757     0.42   0.676    -.8759189    1.350113
     1.Schoolingdum1 |  -.5888421   .2521674    -2.34   0.020    -1.083081    -.094603
     1.Schoolingdum3 |   .1410769   .3446203     0.41   0.682    -.5343664    .8165203
     1.Schoolingdum4 |  -.9006898    .245546    -3.67   0.000    -1.381951   -.4194284
     1.Schoolingdum5 |  -.4106194   .7119385    -0.58   0.564    -1.805993    .9847545
     1.Schoolingdum6 |   .2771312   .2133519     1.30   0.194    -.1410308    .6952931
     1.Schoolingdum7 |  -.0823285   .1929308    -0.43   0.670    -.4604659     .295809
          1.Employed |  -.3472722   .1596173    -2.18   0.030    -.6601163   -.0344281
               _cons |   .0779527   .2734367     0.29   0.776    -.4579734    .6138788
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 933
Model VCE: Robust

Expression: Pr(P1_OutParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition =  .511254 (mean)
    1.Invasion_Condition =  .488746 (mean)
    Age                  =  39.6538 (mean)
    0.Male               = .4951768 (mean)
    1.Male               = .5048232 (mean)
    0.Ethnicitydum1      = .9378349 (mean)
    1.Ethnicitydum1      = .0621651 (mean)
    0.Ethnicitydum2      = .9528403 (mean)
    1.Ethnicitydum2      = .0471597 (mean)
    0.Ethnicitydum3      = .9485531 (mean)
    1.Ethnicitydum3      = .0514469 (mean)
    0.Ethnicitydum4      = .9849946 (mean)
    1.Ethnicitydum4      = .0150054 (mean)
    0.Schoolingdum1      = .8971061 (mean)
    1.Schoolingdum1      = .1028939 (mean)
    0.Schoolingdum3      = .9571275 (mean)
    1.Schoolingdum3      = .0428725 (mean)
    0.Schoolingdum4      = .8788853 (mean)
    1.Schoolingdum4      = .1211147 (mean)
    0.Schoolingdum5      = .9914255 (mean)
    1.Schoolingdum5      = .0085745 (mean)
    0.Schoolingdum6      =   .86388 (mean)
    1.Schoolingdum6      =   .13612 (mean)
    0.Schoolingdum7      = .8199357 (mean)
    1.Schoolingdum7      = .1800643 (mean)
    0.Employed           =  .278671 (mean)
    1.Employed           =  .721329 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0257081   .0331384     0.78   0.438    -.0392419    .0906581
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. logit P1_OutParty i.Invasion_Condition $controls2_logit if Control_Condition==0 , robust

Iteration 0:  Log pseudolikelihood = -630.94697  
Iteration 1:  Log pseudolikelihood =  -603.5358  
Iteration 2:  Log pseudolikelihood = -603.36262  
Iteration 3:  Log pseudolikelihood = -603.36262  

Logistic regression                                     Number of obs =    933
                                                        Wald chi2(17) =  51.55
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -603.36262                       Pseudo R2     = 0.0437

--------------------------------------------------------------------------------------
                     |               Robust
         P1_OutParty | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .1183161   .1386526     0.85   0.393     -.153438    .3900702
                 Age |  -.0083056   .0050161    -1.66   0.098     -.018137    .0015258
              1.Male |   .4959574   .1427421     3.47   0.001      .216188    .7757267
     1.Ethnicitydum1 |   .0886363   .2907428     0.30   0.760     -.481209    .6584817
     1.Ethnicitydum2 |  -.7333795   .3622638    -2.02   0.043    -1.443404   -.0233554
     1.Ethnicitydum3 |  -.2424269   .3311342    -0.73   0.464     -.891438    .4065843
     1.Ethnicitydum4 |   .1509073   .5542201     0.27   0.785     -.935344    1.237159
     1.Schoolingdum1 |  -.5664445   .2543277    -2.23   0.026    -1.064918   -.0679713
     1.Schoolingdum3 |   .1163881    .345396     0.34   0.736    -.5605756    .7933517
     1.Schoolingdum4 |  -.8752619   .2445107    -3.58   0.000    -1.354494   -.3960297
     1.Schoolingdum5 |  -.4216179   .7035008    -0.60   0.549    -1.800454    .9572183
     1.Schoolingdum6 |   .2726694   .2154697     1.27   0.206    -.1496434    .6949822
     1.Schoolingdum7 |  -.0658263   .1940404    -0.34   0.734    -.4461386    .3144859
          1.Employed |   -.355385   .1605233    -2.21   0.027    -.6700049    -.040765
  Political_Interest |   .0365906   .0785156     0.47   0.641    -.1172971    .1904784
   1.StrongSupporter |   -.262608   .1508178    -1.74   0.082    -.5582055    .0329895
          1.Democrat |   .2626371    .141693     1.85   0.064     -.015076    .5403502
               _cons |   -.074032   .3305452    -0.22   0.823    -.7218886    .5738247
--------------------------------------------------------------------------------------

. margins, dydx(Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 933
Model VCE: Robust

Expression: Pr(P1_OutParty), predict()
dy/dx wrt:  1.Invasion_Condition
At: 0.Invasion_Condition =  .511254 (mean)
    1.Invasion_Condition =  .488746 (mean)
    Age                  =  39.6538 (mean)
    0.Male               = .4951768 (mean)
    1.Male               = .5048232 (mean)
    0.Ethnicitydum1      = .9378349 (mean)
    1.Ethnicitydum1      = .0621651 (mean)
    0.Ethnicitydum2      = .9528403 (mean)
    1.Ethnicitydum2      = .0471597 (mean)
    0.Ethnicitydum3      = .9485531 (mean)
    1.Ethnicitydum3      = .0514469 (mean)
    0.Ethnicitydum4      = .9849946 (mean)
    1.Ethnicitydum4      = .0150054 (mean)
    0.Schoolingdum1      = .8971061 (mean)
    1.Schoolingdum1      = .1028939 (mean)
    0.Schoolingdum3      = .9571275 (mean)
    1.Schoolingdum3      = .0428725 (mean)
    0.Schoolingdum4      = .8788853 (mean)
    1.Schoolingdum4      = .1211147 (mean)
    0.Schoolingdum5      = .9914255 (mean)
    1.Schoolingdum5      = .0085745 (mean)
    0.Schoolingdum6      =   .86388 (mean)
    1.Schoolingdum6      =   .13612 (mean)
    0.Schoolingdum7      = .8199357 (mean)
    1.Schoolingdum7      = .1800643 (mean)
    0.Employed           =  .278671 (mean)
    1.Employed           =  .721329 (mean)
    Political_Interest   = 3.315113 (mean)
    0.StrongSupporter    = .5209003 (mean)
    1.StrongSupporter    = .4790997 (mean)
    0.Democrat           = .4908896 (mean)
    1.Democrat           = .5091104 (mean)

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
1.Invasion_Condition |   .0284309   .0333024     0.85   0.393    -.0368405    .0937024
--------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *Multinomial logit regression (treatment effects on Player 1 type in aBoS)
. label define P1types 1 "BB" 2 "BA" 3 "AA" 4 "AB"

. label values P1_Type P1types    //Label 'types'

. 
. mlogit P1_Type i.Invasion_Condition if Disagreement_Condition==0, base(1) robust

Iteration 0:  Log pseudolikelihood =  -1023.072  
Iteration 1:  Log pseudolikelihood = -1020.8931  
Iteration 2:  Log pseudolikelihood = -1020.8916  
Iteration 3:  Log pseudolikelihood = -1020.8916  

Multinomial logistic regression                         Number of obs =    926
                                                        Wald chi2(3)  =   4.34
                                                        Prob > chi2   = 0.2269
Log pseudolikelihood = -1020.8916                       Pseudo R2     = 0.0021

--------------------------------------------------------------------------------------
                     |               Robust
             P1_Type | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
BB                   |  (base outcome)
---------------------+----------------------------------------------------------------
BA                   |
1.Invasion_Condition |   -.087342   .2202491    -0.40   0.692    -.5190222    .3443382
               _cons |  -1.023105   .1573038    -6.50   0.000    -1.331415   -.7147949
---------------------+----------------------------------------------------------------
AA                   |
1.Invasion_Condition |  -.2690806   .1460502    -1.84   0.065    -.5553338    .0171725
               _cons |   .4708203   .1030973     4.57   0.000     .2687532    .6728873
---------------------+----------------------------------------------------------------
AB                   |
1.Invasion_Condition |   .1524686   .3417931     0.45   0.656    -.5174336    .8223708
               _cons |  -2.197225   .2557932    -8.59   0.000     -2.69857   -1.695879
--------------------------------------------------------------------------------------

. margins, dydx(i.Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 926
Model VCE: Robust

dy/dx wrt: 1.Invasion_Condition

1._predict: Pr(P1_Type==BB), predict(pr outcome(1))
2._predict: Pr(P1_Type==BA), predict(pr outcome(2))
3._predict: Pr(P1_Type==AA), predict(pr outcome(3))
4._predict: Pr(P1_Type==AB), predict(pr outcome(4))

At: 0.Invasion_Condition = .5075594 (mean)
    1.Invasion_Condition = .4924406 (mean)

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
0.Invasion_Condition  |  (base outcome)
----------------------+----------------------------------------------------------------
1.Invasion_Condition  |
             _predict |
                   1  |   .0472751   .0313206     1.51   0.131    -.0141121    .1086623
                   2  |   .0057857   .0213677     0.27   0.787    -.0360942    .0476657
                   3  |  -.0651362   .0328046    -1.99   0.047    -.1294321   -.0008404
                   4  |   .0120754    .013231     0.91   0.361     -.013857    .0380078
---------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. mlogit P1_Type i.Invasion_Condition $controls1_logit if Disagreement_Condition==0, base(1) robust

Iteration 0:  Log pseudolikelihood =  -1023.072  
Iteration 1:  Log pseudolikelihood = -989.30479  
Iteration 2:  Log pseudolikelihood = -987.93688  
Iteration 3:  Log pseudolikelihood = -987.66658  
Iteration 4:  Log pseudolikelihood = -987.60569  
Iteration 5:  Log pseudolikelihood = -987.59221  
Iteration 6:  Log pseudolikelihood = -987.59031  
Iteration 7:  Log pseudolikelihood = -987.59007  
Iteration 8:  Log pseudolikelihood = -987.59002  
Iteration 9:  Log pseudolikelihood = -987.59001  

Multinomial logistic regression                        Number of obs =     926
                                                       Wald chi2(42) = 5672.78
                                                       Prob > chi2   =  0.0000
Log pseudolikelihood = -987.59001                      Pseudo R2     =  0.0347

--------------------------------------------------------------------------------------
                     |               Robust
             P1_Type | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
BB                   |  (base outcome)
---------------------+----------------------------------------------------------------
BA                   |
1.Invasion_Condition |  -.0732532   .2265551    -0.32   0.746     -.517293    .3707866
                 Age |   .0140164   .0080598     1.74   0.082    -.0017805    .0298133
              1.Male |  -.2744681   .2235954    -1.23   0.220    -.7127071    .1637709
     1.Ethnicitydum1 |   .0619678   .4309228     0.14   0.886    -.7826254    .9065609
     1.Ethnicitydum2 |   .3985453   .5670104     0.70   0.482    -.7127746    1.509865
     1.Ethnicitydum3 |  -.2848757   .5716283    -0.50   0.618    -1.405247    .8354952
     1.Ethnicitydum4 |   .8289015   .8033789     1.03   0.302    -.7456923    2.403495
     1.Schoolingdum1 |  -.3678458   .4892275    -0.75   0.452    -1.326714    .5910224
     1.Schoolingdum3 |   .2152566   .5269851     0.41   0.683    -.8176153    1.248129
     1.Schoolingdum4 |    .435732   .3814806     1.14   0.253    -.3119563     1.18342
     1.Schoolingdum5 |  -13.84131   .6918024   -20.01   0.000    -15.19722    -12.4854
     1.Schoolingdum6 |  -.2615749   .3447246    -0.76   0.448    -.9372227    .4140729
     1.Schoolingdum7 |  -.0205613   .2891728    -0.07   0.943    -.5873295     .546207
          1.Employed |   .1022322   .2583015     0.40   0.692    -.4040294    .6084939
               _cons |  -1.523076   .4978986    -3.06   0.002     -2.49894    -.547213
---------------------+----------------------------------------------------------------
AA                   |
1.Invasion_Condition |  -.3058017   .1493135    -2.05   0.041    -.5984508   -.0131526
                 Age |   .0124969   .0054482     2.29   0.022     .0018186    .0231751
              1.Male |  -.6157532    .153112    -4.02   0.000    -.9158471   -.3156592
     1.Ethnicitydum1 |    .071252   .2855006     0.25   0.803     -.488319    .6308229
     1.Ethnicitydum2 |   .7693308   .3857007     1.99   0.046     .0133713     1.52529
     1.Ethnicitydum3 |   .3193115   .3239203     0.99   0.324    -.3155607    .9541836
     1.Ethnicitydum4 |  -.1937721   .7500303    -0.26   0.796    -1.663805     1.27626
     1.Schoolingdum1 |    .184587   .2739669     0.67   0.500    -.3523782    .7215522
     1.Schoolingdum3 |  -.4460643   .4083989    -1.09   0.275    -1.246511    .3543828
     1.Schoolingdum4 |   .2388958   .2696497     0.89   0.376    -.2896079    .7673995
     1.Schoolingdum5 |   .7573843   .7560262     1.00   0.316    -.7243998    2.239168
     1.Schoolingdum6 |  -.5820301   .2351005    -2.48   0.013    -1.042819   -.1212416
     1.Schoolingdum7 |  -.5253355    .204512    -2.57   0.010    -.9261716   -.1244994
          1.Employed |  -.0277906   .1783471    -0.16   0.876    -.3773444    .3217633
               _cons |   .4189928   .3107237     1.35   0.178    -.1900145       1.028
---------------------+----------------------------------------------------------------
AB                   |
1.Invasion_Condition |   .1683075   .3416592     0.49   0.622    -.5013323    .8379473
                 Age |   -.001662   .0131503    -0.13   0.899    -.0274361     .024112
              1.Male |   .1078455   .3585052     0.30   0.764    -.5948117    .8105027
     1.Ethnicitydum1 |  -.4987958   .7558007    -0.66   0.509    -1.980138    .9825463
     1.Ethnicitydum2 |  -13.74669   .3738425   -36.77   0.000    -14.47941   -13.01397
     1.Ethnicitydum3 |   .1990801   .6735532     0.30   0.768     -1.12106     1.51922
     1.Ethnicitydum4 |  -14.17985   .6389489   -22.19   0.000    -15.43216   -12.92753
     1.Schoolingdum1 |   .6380727   .5880258     1.09   0.278    -.5144367    1.790582
     1.Schoolingdum3 |   .3152398   .8380453     0.38   0.707    -1.327299    1.957778
     1.Schoolingdum4 |   .5406362   .5683832     0.95   0.342    -.5733744    1.654647
     1.Schoolingdum5 |   1.570797   1.296428     1.21   0.226    -.9701546    4.111749
     1.Schoolingdum6 |  -.3338977   .6045675    -0.55   0.581    -1.518828    .8510329
     1.Schoolingdum7 |   .1636109    .417437     0.39   0.695    -.6545505    .9817723
          1.Employed |  -.0709356   .3622981    -0.20   0.845    -.7810268    .6391556
               _cons |  -2.242178   .6758083    -3.32   0.001    -3.566738   -.9176177
--------------------------------------------------------------------------------------

. margins, dydx(i.Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 926
Model VCE: Robust

dy/dx wrt: 1.Invasion_Condition

1._predict: Pr(P1_Type==BB), predict(pr outcome(1))
2._predict: Pr(P1_Type==BA), predict(pr outcome(2))
3._predict: Pr(P1_Type==AA), predict(pr outcome(3))
4._predict: Pr(P1_Type==AB), predict(pr outcome(4))

At: 0.Invasion_Condition = .5075594 (mean)
    1.Invasion_Condition = .4924406 (mean)
    Age                  = 40.22678 (mean)
    0.Male               = .5075594 (mean)
    1.Male               = .4924406 (mean)
    0.Ethnicitydum1      = .9265659 (mean)
    1.Ethnicitydum1      = .0734341 (mean)
    0.Ethnicitydum2      = .9524838 (mean)
    1.Ethnicitydum2      = .0475162 (mean)
    0.Ethnicitydum3      = .9427646 (mean)
    1.Ethnicitydum3      = .0572354 (mean)
    0.Ethnicitydum4      =  .987041 (mean)
    1.Ethnicitydum4      =  .012959 (mean)
    0.Schoolingdum1      = .9060475 (mean)
    1.Schoolingdum1      = .0939525 (mean)
    0.Schoolingdum3      =  .962203 (mean)
    1.Schoolingdum3      =  .037797 (mean)
    0.Schoolingdum4      = .8844492 (mean)
    1.Schoolingdum4      = .1155508 (mean)
    0.Schoolingdum5      = .9892009 (mean)
    1.Schoolingdum5      = .0107991 (mean)
    0.Schoolingdum6      = .8639309 (mean)
    1.Schoolingdum6      = .1360691 (mean)
    0.Schoolingdum7      = .7840173 (mean)
    1.Schoolingdum7      = .2159827 (mean)
    0.Employed           = .2807775 (mean)
    1.Employed           = .7192225 (mean)

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
0.Invasion_Condition  |  (base outcome)
----------------------+----------------------------------------------------------------
1.Invasion_Condition  |
             _predict |
                   1  |   .0580992   .0325181     1.79   0.074    -.0056351    .1218334
                   2  |   .0094827   .0201961     0.47   0.639    -.0301009    .0490663
                   3  |  -.0738614   .0338974    -2.18   0.029    -.1402991   -.0074237
                   4  |   .0062795   .0061703     1.02   0.309    -.0058141    .0183731
---------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. mlogit P1_Type i.Invasion_Condition $controls2_logit if Disagreement_Condition==0, base(1)      robust

Iteration 0:  Log pseudolikelihood =  -1023.072  
Iteration 1:  Log pseudolikelihood = -966.92937  
Iteration 2:  Log pseudolikelihood = -965.11392  
Iteration 3:  Log pseudolikelihood = -964.80661  
Iteration 4:  Log pseudolikelihood = -964.73623  
Iteration 5:  Log pseudolikelihood = -964.71892  
Iteration 6:  Log pseudolikelihood = -964.71546  
Iteration 7:  Log pseudolikelihood = -964.71472  
Iteration 8:  Log pseudolikelihood = -964.71455  
Iteration 9:  Log pseudolikelihood = -964.71451  
Iteration 10: Log pseudolikelihood =  -964.7145  

Multinomial logistic regression                        Number of obs =     926
                                                       Wald chi2(51) = 6868.97
                                                       Prob > chi2   =  0.0000
Log pseudolikelihood = -964.7145                       Pseudo R2     =  0.0570

--------------------------------------------------------------------------------------
                     |               Robust
             P1_Type | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
BB                   |  (base outcome)
---------------------+----------------------------------------------------------------
BA                   |
1.Invasion_Condition |  -.0721481   .2279918    -0.32   0.752    -.5190038    .3747075
                 Age |   .0091193   .0080762     1.13   0.259    -.0067098    .0249483
              1.Male |  -.3223203   .2328334    -1.38   0.166    -.7786654    .1340249
     1.Ethnicitydum1 |   .3596135   .4563792     0.79   0.431    -.5348734      1.2541
     1.Ethnicitydum2 |   .5070884   .5861763     0.87   0.387    -.6417961    1.655973
     1.Ethnicitydum3 |  -.3039232   .5671998    -0.54   0.592    -1.415614    .8077679
     1.Ethnicitydum4 |    1.01285   .7552299     1.34   0.180    -.4673733    2.493073
     1.Schoolingdum1 |  -.3192221   .5039495    -0.63   0.526    -1.306945    .6685007
     1.Schoolingdum3 |   .2639289    .527781     0.50   0.617    -.7705029    1.298361
     1.Schoolingdum4 |   .5209662   .3844829     1.35   0.175    -.2326065    1.274539
     1.Schoolingdum5 |  -15.35182   .7158146   -21.45   0.000    -16.75479   -13.94884
     1.Schoolingdum6 |  -.2893135   .3505328    -0.83   0.409    -.9763451    .3977181
     1.Schoolingdum7 |  -.0594054   .2917928    -0.20   0.839    -.6313089    .5124981
          1.Employed |   .0218736   .2611634     0.08   0.933    -.4899974    .5337445
  Political_Interest |   .2050987   .1286264     1.59   0.111    -.0470044    .4572017
   1.StrongSupporter |   .8406322   .2534157     3.32   0.001     .3439464    1.337318
          1.Democrat |  -.2787548    .234873    -1.19   0.235    -.7390974    .1815878
               _cons |  -2.327672   .6161853    -3.78   0.000    -3.535373   -1.119971
---------------------+----------------------------------------------------------------
AA                   |
1.Invasion_Condition |  -.3164622   .1513457    -2.09   0.037    -.6130944     -.01983
                 Age |   .0110202   .0055818     1.97   0.048       .00008    .0219604
              1.Male |  -.6262282   .1558298    -4.02   0.000     -.931649   -.3208075
     1.Ethnicitydum1 |    .234883   .2909241     0.81   0.419    -.3353178    .8050838
     1.Ethnicitydum2 |   1.051322   .3933371     2.67   0.008     .2803954    1.822248
     1.Ethnicitydum3 |   .3433436   .3369524     1.02   0.308    -.3170709    1.003758
     1.Ethnicitydum4 |  -.0155428   .7410733    -0.02   0.983     -1.46802    1.436934
     1.Schoolingdum1 |   .1369397   .2806422     0.49   0.626    -.4131089    .6869884
     1.Schoolingdum3 |  -.3232408   .4159982    -0.78   0.437    -1.138582    .4921006
     1.Schoolingdum4 |   .2090958     .26961     0.78   0.438    -.3193302    .7375218
     1.Schoolingdum5 |   .8025396   .7850708     1.02   0.307    -.7361709     2.34125
     1.Schoolingdum6 |  -.5505804   .2397503    -2.30   0.022    -1.020482   -.0806785
     1.Schoolingdum7 |  -.5698963   .2065972    -2.76   0.006    -.9748194   -.1649731
          1.Employed |  -.0622765   .1791214    -0.35   0.728    -.4133481    .2887951
  Political_Interest |  -.0356443   .0877866    -0.41   0.685     -.207703    .1364143
   1.StrongSupporter |   .0990739   .1669038     0.59   0.553    -.2280516    .4261993
          1.Democrat |  -.6751574   .1580181    -4.27   0.000    -.9848673   -.3654476
               _cons |   .9164057   .4009037     2.29   0.022     .1306489    1.702163
---------------------+----------------------------------------------------------------
AB                   |
1.Invasion_Condition |   .1578708   .3447416     0.46   0.647    -.5178104     .833552
                 Age |  -.0040449   .0130282    -0.31   0.756    -.0295797    .0214899
              1.Male |   .0686787   .3669082     0.19   0.852    -.6504482    .7878056
     1.Ethnicitydum1 |  -.2533022   .7613291    -0.33   0.739     -1.74548    1.238875
     1.Ethnicitydum2 |  -14.99579   .4146882   -36.16   0.000    -15.80856   -14.18301
     1.Ethnicitydum3 |   .2468861   .6771865     0.36   0.715    -1.080375    1.574147
     1.Ethnicitydum4 |  -15.56394   .6602921   -23.57   0.000    -16.85809    -14.2698
     1.Schoolingdum1 |   .5582574   .5863504     0.95   0.341    -.5909682    1.707483
     1.Schoolingdum3 |    .436477   .8372591     0.52   0.602    -1.204521    2.077475
     1.Schoolingdum4 |   .5171579   .5722996     0.90   0.366    -.6045288    1.638845
     1.Schoolingdum5 |   1.651981   1.339246     1.23   0.217    -.9728921    4.276855
     1.Schoolingdum6 |  -.3226956   .6062224    -0.53   0.595     -1.51087    .8654784
     1.Schoolingdum7 |   .1046835   .4218314     0.25   0.804    -.7220909    .9314579
          1.Employed |  -.1106935   .3666935    -0.30   0.763    -.8293996    .6080127
  Political_Interest |   .0339229   .1638643     0.21   0.836    -.2872453     .355091
   1.StrongSupporter |  -.0416954   .3594142    -0.12   0.908    -.7461343    .6627435
          1.Democrat |  -.8898617   .3714776    -2.40   0.017    -1.617944   -.1617789
               _cons |  -1.773647   .8557451    -2.07   0.038    -3.450877   -.0964178
--------------------------------------------------------------------------------------

. margins, dydx(i.Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 926
Model VCE: Robust

dy/dx wrt: 1.Invasion_Condition

1._predict: Pr(P1_Type==BB), predict(pr outcome(1))
2._predict: Pr(P1_Type==BA), predict(pr outcome(2))
3._predict: Pr(P1_Type==AA), predict(pr outcome(3))
4._predict: Pr(P1_Type==AB), predict(pr outcome(4))

At: 0.Invasion_Condition = .5075594 (mean)
    1.Invasion_Condition = .4924406 (mean)
    Age                  = 40.22678 (mean)
    0.Male               = .5075594 (mean)
    1.Male               = .4924406 (mean)
    0.Ethnicitydum1      = .9265659 (mean)
    1.Ethnicitydum1      = .0734341 (mean)
    0.Ethnicitydum2      = .9524838 (mean)
    1.Ethnicitydum2      = .0475162 (mean)
    0.Ethnicitydum3      = .9427646 (mean)
    1.Ethnicitydum3      = .0572354 (mean)
    0.Ethnicitydum4      =  .987041 (mean)
    1.Ethnicitydum4      =  .012959 (mean)
    0.Schoolingdum1      = .9060475 (mean)
    1.Schoolingdum1      = .0939525 (mean)
    0.Schoolingdum3      =  .962203 (mean)
    1.Schoolingdum3      =  .037797 (mean)
    0.Schoolingdum4      = .8844492 (mean)
    1.Schoolingdum4      = .1155508 (mean)
    0.Schoolingdum5      = .9892009 (mean)
    1.Schoolingdum5      = .0107991 (mean)
    0.Schoolingdum6      = .8639309 (mean)
    1.Schoolingdum6      = .1360691 (mean)
    0.Schoolingdum7      = .7840173 (mean)
    1.Schoolingdum7      = .2159827 (mean)
    0.Employed           = .2807775 (mean)
    1.Employed           = .7192225 (mean)
    Political_Interest   =  3.37257 (mean)
    0.StrongSupporter    = .5064795 (mean)
    1.StrongSupporter    = .4935205 (mean)
    0.Democrat           = .4902808 (mean)
    1.Democrat           = .5097192 (mean)

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
0.Invasion_Condition  |  (base outcome)
----------------------+----------------------------------------------------------------
1.Invasion_Condition  |
             _predict |
                   1  |   .0614274   .0330713     1.86   0.063    -.0033912     .126246
                   2  |   .0095139   .0187665     0.51   0.612    -.0272677    .0462956
                   3  |  -.0765524   .0344324    -2.22   0.026    -.1440387   -.0090662
                   4  |   .0056111   .0055718     1.01   0.314    -.0053095    .0165317
---------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. mlogit P1_Type i.Invasion_Condition if Control_Condition==0, base(1) robust

Iteration 0:  Log pseudolikelihood = -1019.2913  
Iteration 1:  Log pseudolikelihood = -1017.2683  
Iteration 2:  Log pseudolikelihood = -1017.2429  
Iteration 3:  Log pseudolikelihood = -1017.2429  

Multinomial logistic regression                         Number of obs =    933
                                                        Wald chi2(3)  =   3.99
                                                        Prob > chi2   = 0.2623
Log pseudolikelihood = -1017.2429                       Pseudo R2     = 0.0020

--------------------------------------------------------------------------------------
                     |               Robust
             P1_Type | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
BB                   |  (base outcome)
---------------------+----------------------------------------------------------------
BA                   |
1.Invasion_Condition |   .1282116    .221544     0.58   0.563    -.3060067      .56243
               _cons |  -1.238658   .1591125    -7.78   0.000    -1.550513   -.9268035
---------------------+----------------------------------------------------------------
AA                   |
1.Invasion_Condition |  -.0958365   .1435736    -0.67   0.504    -.3772355    .1855625
               _cons |   .2975761   .0995583     2.99   0.003     .1024455    .4927068
---------------------+----------------------------------------------------------------
AB                   |
1.Invasion_Condition |   .5607783   .3661768     1.53   0.126     -.156915    1.278472
               _cons |  -2.605534   .2875647    -9.06   0.000    -3.169151   -2.041918
--------------------------------------------------------------------------------------

. margins, dydx(i.Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 933
Model VCE: Robust

dy/dx wrt: 1.Invasion_Condition

1._predict: Pr(P1_Type==BB), predict(pr outcome(1))
2._predict: Pr(P1_Type==BA), predict(pr outcome(2))
3._predict: Pr(P1_Type==AA), predict(pr outcome(3))
4._predict: Pr(P1_Type==AB), predict(pr outcome(4))

At: 0.Invasion_Condition = .511254 (mean)
    1.Invasion_Condition = .488746 (mean)

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
0.Invasion_Condition  |  (base outcome)
----------------------+----------------------------------------------------------------
1.Invasion_Condition  |
             _predict |
                   1  |   .0038343   .0316537     0.12   0.904    -.0582059    .0658744
                   2  |   .0158888   .0209019     0.76   0.447    -.0250782    .0568558
                   3  |   -.040715   .0326996    -1.25   0.213     -.104805     .023375
                   4  |   .0209919   .0125077     1.68   0.093    -.0035228    .0455066
---------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. mlogit P1_Type i.Invasion_Condition $controls1_logit if Control_Condition==0, base(1) robust

Iteration 0:  Log pseudolikelihood = -1019.2913  
Iteration 1:  Log pseudolikelihood = -976.34558  
Iteration 2:  Log pseudolikelihood =  -974.4885  
Iteration 3:  Log pseudolikelihood = -974.41603  
Iteration 4:  Log pseudolikelihood = -974.39918  
Iteration 5:  Log pseudolikelihood = -974.39581  
Iteration 6:  Log pseudolikelihood = -974.39523  
Iteration 7:  Log pseudolikelihood =  -974.3951  
Iteration 8:  Log pseudolikelihood = -974.39507  

Multinomial logistic regression                         Number of obs =    933
                                                        Wald chi2(42) = 983.51
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -974.39507                       Pseudo R2     = 0.0440

--------------------------------------------------------------------------------------
                     |               Robust
             P1_Type | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
BB                   |  (base outcome)
---------------------+----------------------------------------------------------------
BA                   |
1.Invasion_Condition |   .1501213   .2246091     0.67   0.504    -.2901045     .590347
                 Age |   .0184215   .0082633     2.23   0.026     .0022257    .0346173
              1.Male |  -.4779318   .2308204    -2.07   0.038    -.9303315   -.0255321
     1.Ethnicitydum1 |  -.5634886   .5553452    -1.01   0.310    -1.651945     .524968
     1.Ethnicitydum2 |   .7703482   .5084192     1.52   0.130    -.2261351    1.766831
     1.Ethnicitydum3 |  -.3266309   .6578654    -0.50   0.620    -1.616023    .9627615
     1.Ethnicitydum4 |   .1158147   .8056087     0.14   0.886    -1.463149    1.694779
     1.Schoolingdum1 |   .4427174   .4626995     0.96   0.339     -.464157    1.349592
     1.Schoolingdum3 |  -.3298095   .6498707    -0.51   0.612    -1.603533    .9439137
     1.Schoolingdum4 |   1.198339   .3803579     3.15   0.002     .4528514    1.943827
     1.Schoolingdum5 |  -10.95356   .6926424   -15.81   0.000    -12.31111   -9.596003
     1.Schoolingdum6 |    .184924   .3405312     0.54   0.587    -.4825049    .8523529
     1.Schoolingdum7 |   .7990151   .2910002     2.75   0.006     .2286651    1.369365
          1.Employed |   .7257603   .2712294     2.68   0.007     .1941604     1.25736
               _cons |  -2.639506   .5175301    -5.10   0.000    -3.653847   -1.625166
---------------------+----------------------------------------------------------------
AA                   |
1.Invasion_Condition |  -.1028477   .1486851    -0.69   0.489    -.3942651    .1885698
                 Age |   .0080422   .0051938     1.55   0.122    -.0021375     .018222
              1.Male |  -.4677773     .14928    -3.13   0.002    -.7603608   -.1751939
     1.Ethnicitydum1 |  -.1521008   .2964187    -0.51   0.608    -.7330707    .4288691
     1.Ethnicitydum2 |   .6570044   .3840214     1.71   0.087    -.0956637    1.409673
     1.Ethnicitydum3 |   .4596594   .3526459     1.30   0.192     -.231514    1.150833
     1.Ethnicitydum4 |  -.2380179   .6179296    -0.39   0.700    -1.449138    .9731018
     1.Schoolingdum1 |   .7829414    .276687     2.83   0.005     .2406448    1.325238
     1.Schoolingdum3 |  -.1599449   .3611213    -0.44   0.658    -.8677295    .5478398
     1.Schoolingdum4 |   .8745901   .2640536     3.31   0.001     .3570546    1.392126
     1.Schoolingdum5 |   .8829441   .7934431     1.11   0.266    -.6721758    2.438064
     1.Schoolingdum6 |  -.4391291   .2311773    -1.90   0.057    -.8922283    .0139701
     1.Schoolingdum7 |  -.1397742   .2145843    -0.65   0.515    -.5603517    .2808032
          1.Employed |   .2854461   .1722508     1.66   0.097    -.0521592    .6230513
               _cons |  -.1044531   .2919657    -0.36   0.721    -.6766953    .4677891
---------------------+----------------------------------------------------------------
AB                   |
1.Invasion_Condition |   .5782765   .3718032     1.56   0.120    -.1504445    1.306997
                 Age |   .0093958   .0145768     0.64   0.519    -.0191742    .0379658
              1.Male |   .5380732   .4016883     1.34   0.180    -.2492214    1.325368
     1.Ethnicitydum1 |  -.9544157    1.03473    -0.92   0.356    -2.982449    1.073618
     1.Ethnicitydum2 |    -.12211   1.151987    -0.11   0.916    -2.379964    2.135744
     1.Ethnicitydum3 |   .7367666   .6777225     1.09   0.277    -.5915451    2.065078
     1.Ethnicitydum4 |   .7306769   1.122155     0.65   0.515    -1.468706     2.93006
     1.Schoolingdum1 |   1.053997   .5574194     1.89   0.059    -.0385247    2.146519
     1.Schoolingdum3 |  -.6654257   1.091951    -0.61   0.542    -2.805609    1.474758
     1.Schoolingdum4 |   .3367037   .6651503     0.51   0.613    -.9669669    1.640374
     1.Schoolingdum5 |   1.544902   1.296828     1.19   0.234    -.9968342    4.086638
     1.Schoolingdum6 |  -.4088694   .5967025    -0.69   0.493    -1.578385    .7606459
     1.Schoolingdum7 |  -.1875457    .521738    -0.36   0.719    -1.210133     .835042
          1.Employed |   .2544526   .4098661     0.62   0.535    -.5488702    1.057775
               _cons |  -3.571424   .7033476    -5.08   0.000     -4.94996   -2.192888
--------------------------------------------------------------------------------------

. margins, dydx(i.Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 933
Model VCE: Robust

dy/dx wrt: 1.Invasion_Condition

1._predict: Pr(P1_Type==BB), predict(pr outcome(1))
2._predict: Pr(P1_Type==BA), predict(pr outcome(2))
3._predict: Pr(P1_Type==AA), predict(pr outcome(3))
4._predict: Pr(P1_Type==AB), predict(pr outcome(4))

At: 0.Invasion_Condition =  .511254 (mean)
    1.Invasion_Condition =  .488746 (mean)
    Age                  =  39.6538 (mean)
    0.Male               = .4951768 (mean)
    1.Male               = .5048232 (mean)
    0.Ethnicitydum1      = .9378349 (mean)
    1.Ethnicitydum1      = .0621651 (mean)
    0.Ethnicitydum2      = .9528403 (mean)
    1.Ethnicitydum2      = .0471597 (mean)
    0.Ethnicitydum3      = .9485531 (mean)
    1.Ethnicitydum3      = .0514469 (mean)
    0.Ethnicitydum4      = .9849946 (mean)
    1.Ethnicitydum4      = .0150054 (mean)
    0.Schoolingdum1      = .8971061 (mean)
    1.Schoolingdum1      = .1028939 (mean)
    0.Schoolingdum3      = .9571275 (mean)
    1.Schoolingdum3      = .0428725 (mean)
    0.Schoolingdum4      = .8788853 (mean)
    1.Schoolingdum4      = .1211147 (mean)
    0.Schoolingdum5      = .9914255 (mean)
    1.Schoolingdum5      = .0085745 (mean)
    0.Schoolingdum6      =   .86388 (mean)
    1.Schoolingdum6      =   .13612 (mean)
    0.Schoolingdum7      = .8199357 (mean)
    1.Schoolingdum7      = .1800643 (mean)
    0.Employed           =  .278671 (mean)
    1.Employed           =  .721329 (mean)

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
0.Invasion_Condition  |  (base outcome)
----------------------+----------------------------------------------------------------
1.Invasion_Condition  |
             _predict |
                   1  |   .0060445   .0327951     0.18   0.854    -.0582328    .0703217
                   2  |   .0166893    .018774     0.89   0.374     -.020107    .0534856
                   3  |  -.0426113   .0338627    -1.26   0.208     -.108981    .0237584
                   4  |   .0198776   .0115989     1.71   0.087    -.0028558     .042611
---------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. mlogit P1_Type i.Invasion_Condition $controls2_logit if Control_Condition==0, base(1) robust

Iteration 0:  Log pseudolikelihood = -1019.2913  
Iteration 1:  Log pseudolikelihood = -964.60864  
Iteration 2:  Log pseudolikelihood = -962.03206  
Iteration 3:  Log pseudolikelihood = -961.95342  
Iteration 4:  Log pseudolikelihood = -961.93474  
Iteration 5:  Log pseudolikelihood = -961.93095  
Iteration 6:  Log pseudolikelihood = -961.93015  
Iteration 7:  Log pseudolikelihood = -961.92996  
Iteration 8:  Log pseudolikelihood = -961.92992  
Iteration 9:  Log pseudolikelihood = -961.92991  

Multinomial logistic regression                        Number of obs =     933
                                                       Wald chi2(51) = 1421.53
                                                       Prob > chi2   =  0.0000
Log pseudolikelihood = -961.92991                      Pseudo R2     =  0.0563

--------------------------------------------------------------------------------------
                     |               Robust
             P1_Type | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------------+----------------------------------------------------------------
BB                   |  (base outcome)
---------------------+----------------------------------------------------------------
BA                   |
1.Invasion_Condition |   .1187775   .2254976     0.53   0.598    -.3231896    .5607447
                 Age |   .0163443   .0083654     1.95   0.051    -.0000516    .0327402
              1.Male |  -.4266008   .2431499    -1.75   0.079    -.9031658    .0499643
     1.Ethnicitydum1 |  -.5196469   .5846229    -0.89   0.374    -1.665487    .6261929
     1.Ethnicitydum2 |   .7054084   .5080542     1.39   0.165    -.2903596    1.701176
     1.Ethnicitydum3 |  -.3970768   .6628967    -0.60   0.549     -1.69633    .9021769
     1.Ethnicitydum4 |   .1837465   .8164876     0.23   0.822     -1.41654    1.784033
     1.Schoolingdum1 |   .4678319   .4750938     0.98   0.325    -.4633349    1.398999
     1.Schoolingdum3 |  -.2877444    .657535    -0.44   0.662    -1.576489    1.001001
     1.Schoolingdum4 |    1.22025   .3826089     3.19   0.001       .47035    1.970149
     1.Schoolingdum5 |  -13.50114   .6770472   -19.94   0.000    -14.82813   -12.17415
     1.Schoolingdum6 |   .1720629   .3433322     0.50   0.616    -.5008558    .8449816
     1.Schoolingdum7 |   .8100906   .2932095     2.76   0.006     .2354105    1.384771
          1.Employed |   .7407349   .2715648     2.73   0.006     .2084776    1.272992
  Political_Interest |  -.0254152   .1335714    -0.19   0.849    -.2872102    .2363798
   1.StrongSupporter |   .8037105   .2439304     3.29   0.001     .3256157    1.281805
          1.Democrat |  -.0407413   .2396765    -0.17   0.865    -.5104986    .4290161
               _cons |  -2.918108   .5966771    -4.89   0.000    -4.087573   -1.748642
---------------------+----------------------------------------------------------------
AA                   |
1.Invasion_Condition |  -.1063616   .1497054    -0.71   0.477    -.3997788    .1870555
                 Age |   .0073816   .0053595     1.38   0.168    -.0031228    .0178861
              1.Male |   -.443429   .1531177    -2.90   0.004    -.7435341   -.1433239
     1.Ethnicitydum1 |  -.0604289   .3049661    -0.20   0.843    -.6581514    .5372936
     1.Ethnicitydum2 |   .7685964   .3828599     2.01   0.045     .0182048    1.518988
     1.Ethnicitydum3 |   .4557632   .3615485     1.26   0.207    -.2528588    1.164385
     1.Ethnicitydum4 |  -.1379194   .6174513    -0.22   0.823    -1.348102    1.072263
     1.Schoolingdum1 |   .7439025   .2797319     2.66   0.008      .195638    1.292167
     1.Schoolingdum3 |   -.132248   .3625201    -0.36   0.715    -.8427743    .5782784
     1.Schoolingdum4 |    .837634   .2643155     3.17   0.002     .3195851    1.355683
     1.Schoolingdum5 |   .9037344    .787597     1.15   0.251    -.6399274    2.447396
     1.Schoolingdum6 |  -.4262294   .2333544    -1.83   0.068    -.8835957    .0311369
     1.Schoolingdum7 |  -.1611035   .2161068    -0.75   0.456     -.584665    .2624581
          1.Employed |   .2946903   .1731397     1.70   0.089    -.0446572    .6340378
  Political_Interest |   -.043107   .0850617    -0.51   0.612    -.2098249    .1236109
   1.StrongSupporter |   .1236786   .1639631     0.75   0.451    -.1976832    .4450405
          1.Democrat |  -.4071948   .1532682    -2.66   0.008     -.707595   -.1067945
               _cons |   .1963858   .3593304     0.55   0.585    -.5078889    .9006604
---------------------+----------------------------------------------------------------
AB                   |
1.Invasion_Condition |    .583681   .3761488     1.55   0.121    -.1535571    1.320919
                 Age |   .0079096   .0147556     0.54   0.592    -.0210108    .0368299
              1.Male |   .5454483   .4244576     1.29   0.199    -.2864732     1.37737
     1.Ethnicitydum1 |  -.7387647   1.032591    -0.72   0.474    -2.762607    1.285077
     1.Ethnicitydum2 |   .1885718   1.133507     0.17   0.868    -2.033062    2.410205
     1.Ethnicitydum3 |   .7401285    .669796     1.11   0.269    -.5726475    2.052904
     1.Ethnicitydum4 |    1.00077    1.12319     0.89   0.373    -1.200641    3.202181
     1.Schoolingdum1 |   .9588583   .5536473     1.73   0.083    -.1262704    2.043987
     1.Schoolingdum3 |  -.6515028   1.092019    -0.60   0.551    -2.791821    1.488815
     1.Schoolingdum4 |   .2814354   .6780606     0.42   0.678    -1.047539     1.61041
     1.Schoolingdum5 |   1.602695   1.354879     1.18   0.237    -1.052819    4.258209
     1.Schoolingdum6 |  -.4176003   .5933034    -0.70   0.482    -1.580454     .745253
     1.Schoolingdum7 |  -.2220296   .5273023    -0.42   0.674    -1.255523    .8114639
          1.Employed |   .2657608   .4165724     0.64   0.523    -.5507061    1.082228
  Political_Interest |   .0118093    .176015     0.07   0.947    -.3331737    .3567924
   1.StrongSupporter |  -.1411593   .3651102    -0.39   0.699    -.8567622    .5744437
          1.Democrat |  -.8560156   .3799005    -2.25   0.024    -1.600607   -.1114243
               _cons |  -3.125091   .8381054    -3.73   0.000    -4.767748   -1.482435
--------------------------------------------------------------------------------------

. margins, dydx(i.Invasion_Condition) atmeans post

Conditional marginal effects                               Number of obs = 933
Model VCE: Robust

dy/dx wrt: 1.Invasion_Condition

1._predict: Pr(P1_Type==BB), predict(pr outcome(1))
2._predict: Pr(P1_Type==BA), predict(pr outcome(2))
3._predict: Pr(P1_Type==AA), predict(pr outcome(3))
4._predict: Pr(P1_Type==AB), predict(pr outcome(4))

At: 0.Invasion_Condition =  .511254 (mean)
    1.Invasion_Condition =  .488746 (mean)
    Age                  =  39.6538 (mean)
    0.Male               = .4951768 (mean)
    1.Male               = .5048232 (mean)
    0.Ethnicitydum1      = .9378349 (mean)
    1.Ethnicitydum1      = .0621651 (mean)
    0.Ethnicitydum2      = .9528403 (mean)
    1.Ethnicitydum2      = .0471597 (mean)
    0.Ethnicitydum3      = .9485531 (mean)
    1.Ethnicitydum3      = .0514469 (mean)
    0.Ethnicitydum4      = .9849946 (mean)
    1.Ethnicitydum4      = .0150054 (mean)
    0.Schoolingdum1      = .8971061 (mean)
    1.Schoolingdum1      = .1028939 (mean)
    0.Schoolingdum3      = .9571275 (mean)
    1.Schoolingdum3      = .0428725 (mean)
    0.Schoolingdum4      = .8788853 (mean)
    1.Schoolingdum4      = .1211147 (mean)
    0.Schoolingdum5      = .9914255 (mean)
    1.Schoolingdum5      = .0085745 (mean)
    0.Schoolingdum6      =   .86388 (mean)
    1.Schoolingdum6      =   .13612 (mean)
    0.Schoolingdum7      = .8199357 (mean)
    1.Schoolingdum7      = .1800643 (mean)
    0.Employed           =  .278671 (mean)
    1.Employed           =  .721329 (mean)
    Political_Interest   = 3.315113 (mean)
    0.StrongSupporter    = .5209003 (mean)
    1.StrongSupporter    = .4790997 (mean)
    0.Democrat           = .4908896 (mean)
    1.Democrat           = .5091104 (mean)

---------------------------------------------------------------------------------------
                      |            Delta-method
                      |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
0.Invasion_Condition  |  (base outcome)
----------------------+----------------------------------------------------------------
1.Invasion_Condition  |
             _predict |
                   1  |   .0087038   .0331704     0.26   0.793     -.056309    .0737166
                   2  |    .013276   .0176938     0.75   0.453    -.0214033    .0479553
                   3  |  -.0413818   .0340873    -1.21   0.225    -.1081916    .0254281
                   4  |    .019402   .0111104     1.75   0.081     -.002374    .0411779
---------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. 
. *Close log
. log close
      name:  <unnamed>
       log:  C:\Users\Jonas Kaiser\Desktop\Replication\Replication\Replication_Main_Log.log
  log type:  text
 closed on:   6 Feb 2025, 08:49:47
--------------------------------------------------------------------------------------------------------------
